Vincent Selhorst-Jones

Matrices

Slide Duration:

Table of Contents

Section 1: Introduction
Introduction to Precalculus

10m 3s

Intro
0:00
Title of the Course
0:06
Different Names for the Course
0:07
Precalculus
0:12
Math Analysis
0:14
Trigonometry
0:16
Algebra III
0:20
Geometry II
0:24
College Algebra
0:30
Same Concepts
0:36
How do the Lessons Work?
0:54
Introducing Concepts
0:56
Apply Concepts
1:04
Go through Examples
1:25
Who is this Course For?
1:38
Those Who Need eExtra Help with Class Work
1:52
Those Working on Material but not in Formal Class at School
1:54
Those Who Want a Refresher
2:00
Try to Watch the Whole Lesson
2:20
Understanding is So Important
3:56
What to Watch First
5:26
Lesson #2: Sets, Elements, and Numbers
5:30
Lesson #7: Idea of a Function
5:33
Lesson #6: Word Problems
6:04
What to Watch First, cont.
6:46
Lesson #2: Sets, Elements and Numbers
6:56
Lesson #3: Variables, Equations, and Algebra
6:58
Lesson #4: Coordinate Systems
7:00
Lesson #5: Midpoint, Distance, the Pythagorean Theorem and Slope
7:02
Lesson #6: Word Problems
7:10
Lesson #7: Idea of a Function
7:12
Lesson #8: Graphs
7:14
Graphing Calculator Appendix
7:40
What to Watch Last
8:46
Let's get Started!
9:48
Sets, Elements, & Numbers

45m 11s

Intro
0:00
Introduction
0:05
Sets and Elements
1:19
Set
1:20
Element
1:23
Name a Set
2:20
Order The Elements Appear In Has No Effect on the Set
2:55
Describing/ Defining Sets
3:28
Directly Say All the Elements
3:36
Clearly Describing All the Members of the Set
3:55
Describing the Quality (or Qualities) Each member Of the Set Has In Common
4:32
Symbols: 'Element of' and 'Subset of'
6:01
Symbol is ∈
6:03
Subset Symbol is ⊂
6:35
Empty Set
8:07
Symbol is ∅
8:20
Since It's Empty, It is a Subset of All Sets
8:44
Union and Intersection
9:54
Union Symbol is ∪
10:08
Intersection Symbol is ∩
10:18
Sets Can Be Weird Stuff
12:26
Can Have Elements in a Set
12:50
We Can Have Infinite Sets
13:09
Example
13:22
Consider a Set Where We Take a Word and Then Repeat It An Ever Increasing Number of Times
14:08
This Set Has Infinitely Many Distinct Elements
14:40
Numbers as Sets
16:03
Natural Numbers ℕ
16:16
Including 0 and the Negatives ℤ
18:13
Rational Numbers ℚ
19:27
Can Express Rational Numbers with Decimal Expansions
22:05
Irrational Numbers
23:37
Real Numbers ℝ: Put the Rational and Irrational Numbers Together
25:15
Interval Notation and the Real Numbers
26:45
Include the End Numbers
27:06
Exclude the End Numbers
27:33
Example
28:28
Interval Notation: Infinity
29:09
Use -∞ or ∞ to Show an Interval Going on Forever in One Direction or the Other
29:14
Always Use Parentheses
29:50
Examples
30:27
Example 1
31:23
Example 2
35:26
Example 3
38:02
Example 4
42:21
Variables, Equations, & Algebra

35m 31s

Intro
0:00
What is a Variable?
0:05
A Variable is a Placeholder for a Number
0:11
Affects the Output of a Function or a Dependent Variable
0:24
Naming Variables
1:51
Useful to Use Symbols
2:21
What is a Constant?
4:14
A Constant is a Fixed, Unchanging Number
4:28
We Might Refer to a Symbol Representing a Number as a Constant
4:51
What is a Coefficient?
5:33
A Coefficient is a Multiplicative Factor on a Variable
5:37
Not All Coefficients are Constants
5:51
Expressions and Equations
6:42
An Expression is a String of Mathematical Symbols That Make Sense Used Together
7:05
An Equation is a Statement That Two Expression Have the Same Value
8:20
The Idea of Algebra
8:51
Equality
8:59
If Two Things Are the Same *Equal), Then We Can Do the Exact Same Operation to Both and the Results Will Be the Same
9:41
Always Do The Exact Same Thing to Both Sides
12:22
Solving Equations
13:23
When You Are Asked to Solve an Equation, You Are Being Asked to Solve for Something
13:33
Look For What Values Makes the Equation True
13:38
Isolate the Variable by Doing Algebra
14:37
Order of Operations
16:02
Why Certain Operations are Grouped
17:01
When You Don't Have to Worry About Order
17:39
Distributive Property
18:15
It Allows Multiplication to Act Over Addition in Parentheses
18:23
We Can Use the Distributive Property in Reverse to Combine Like Terms
19:05
Substitution
20:03
Use Information From One Equation in Another Equation
20:07
Put Your Substitution in Parentheses
20:44
Example 1
23:17
Example 2
25:49
Example 3
28:11
Example 4
30:02
Coordinate Systems

35m 2s

Intro
0:00
Inherent Order in ℝ
0:05
Real Numbers Come with an Inherent Order
0:11
Positive Numbers
0:21
Negative Numbers
0:58
'Less Than' and 'Greater Than'
2:04
Tip To Help You Remember the Signs
2:56
Inequality
4:06
Less Than or Equal and Greater Than or Equal
4:51
One Dimension: The Number Line
5:36
Graphically Represent ℝ on a Number Line
5:43
Note on Infinities
5:57
With the Number Line, We Can Directly See the Order We Put on ℝ
6:35
Ordered Pairs
7:22
Example
7:34
Allows Us to Talk About Two Numbers at the Same Time
9:41
Ordered Pairs of Real Numbers Cannot be Put Into an Order Like we Did with ℝ
10:41
Two Dimensions: The Plane
13:13
We Can Represent Ordered Pairs with the Plane
13:24
Intersection is known as the Origin
14:31
Plotting the Point
14:32
Plane = Coordinate Plane = Cartesian Plane = ℝ²
17:46
The Plane and Quadrants
18:50
Quadrant I
19:04
Quadrant II
19:21
Quadrant III
20:04
Quadrant IV
20:20
Three Dimensions: Space
21:02
Create Ordered Triplets
21:09
Visually Represent This
21:19
Three-Dimension = Space = ℝ³
21:47
Higher Dimensions
22:24
If We Have n Dimensions, We Call It n-Dimensional Space or ℝ to the nth Power
22:31
We Can Represent Places In This n-Dimensional Space As Ordered Groupings of n Numbers
22:41
Hard to Visualize Higher Dimensional Spaces
23:18
Example 1
25:07
Example 2
26:10
Example 3
28:58
Example 4
31:05
Midpoints, Distance, the Pythagorean Theorem, & Slope

48m 43s

Intro
0:00
Introduction
0:07
Midpoint: One Dimension
2:09
Example of Something More Complex
2:31
Use the Idea of a Middle
3:28
Find the Midpoint of Arbitrary Values a and b
4:17
How They're Equivalent
5:05
Official Midpoint Formula
5:46
Midpoint: Two Dimensions
6:19
The Midpoint Must Occur at the Horizontal Middle and the Vertical Middle
6:38
Arbitrary Pair of Points Example
7:25
Distance: One Dimension
9:26
Absolute Value
10:54
Idea of Forcing Positive
11:06
Distance: One Dimension, Formula
11:47
Distance Between Arbitrary a and b
11:48
Absolute Value Helps When the Distance is Negative
12:41
Distance Formula
12:58
The Pythagorean Theorem
13:24
a²+b²=c²
13:50
Distance: Two Dimensions
14:59
Break Into Horizontal and Vertical Parts and then Use the Pythagorean Theorem
15:16
Distance Between Arbitrary Points (x₁,y₁) and (x₂,y₂)
16:21
Slope
19:30
Slope is the Rate of Change
19:41
m = rise over run
21:27
Slope Between Arbitrary Points (x₁,y₁) and (x₂,y₂)
22:31
Interpreting Slope
24:12
Positive Slope and Negative Slope
25:40
m=1, m=0, m=-1
26:48
Example 1
28:25
Example 2
31:42
Example 3
36:40
Example 4
42:48
Word Problems

56m 31s

Intro
0:00
Introduction
0:05
What is a Word Problem?
0:45
Describes Any Problem That Primarily Gets Its Ideas Across With Words Instead of Math Symbols
0:48
Requires Us to Think
1:32
Why Are They So Hard?
2:11
Reason 1: No Simple Formula to Solve Them
2:16
Reason 2: Harder to Teach Word Problems
2:47
You Can Learn How to Do Them!
3:51
Grades
7:57
'But I'm Never Going to Use This In Real Life'
9:46
Solving Word Problems
12:58
First: Understand the Problem
13:37
Second: What Are You Looking For?
14:33
Third: Set Up Relationships
16:21
Fourth: Solve It!
17:48
Summary of Method
19:04
Examples on Things Other Than Math
20:21
Math-Specific Method: What You Need Now
25:30
Understand What the Problem is Talking About
25:37
Set Up and Name Any Variables You Need to Know
25:56
Set Up Equations Connecting Those Variables to the Information in the Problem Statement
26:02
Use the Equations to Solve for an Answer
26:14
Tip
26:58
Draw Pictures
27:22
Breaking Into Pieces
28:28
Try Out Hypothetical Numbers
29:52
Student Logic
31:27
Jump In!
32:40
Example 1
34:03
Example 2
39:15
Example 3
44:22
Example 4
50:24
Section 2: Functions
Idea of a Function

39m 54s

Intro
0:00
Introduction
0:04
What is a Function?
1:06
A Visual Example and Non-Example
1:30
Function Notation
3:47
f(x)
4:05
Express What Sets the Function Acts On
5:45
Metaphors for a Function
6:17
Transformation
6:28
Map
7:17
Machine
8:56
Same Input Always Gives Same Output
10:01
If We Put the Same Input Into a Function, It Will Always Produce the Same Output
10:11
Example of Something That is Not a Function
11:10
A Non-Numerical Example
12:10
The Functions We Will Use
15:05
Unless Told Otherwise, We Will Assume Every Function Takes in Real Numbers and Outputs Real Numbers
15:11
Usually Told the Rule of a Given Function
15:27
How To Use a Function
16:18
Apply the Rule to Whatever Our Input Value Is
16:28
Make Sure to Wrap Your Substitutions in Parentheses
17:09
Functions and Tables
17:36
Table of Values, Sometimes Called a T-Table
17:46
Example
17:56
Domain: What Goes In
18:55
The Domain is the Set of all Inputs That the Function Can Accept
18:56
Example
19:40
Range: What Comes Out
21:27
The Range is the Set of All Possible Outputs a Function Can Assign
21:34
Example
21:49
Another Example Would Be Our Initial Function From Earlier in This Lesson
22:29
Example 1
23:45
Example 2
25:22
Example 3
27:27
Example 4
29:23
Example 5
33:33
Graphs

58m 26s

Intro
0:00
Introduction
0:04
How to Interpret Graphs
1:17
Input / Independent Variable
1:47
Output / Dependent Variable
2:00
Graph as Input ⇒ Output
2:23
One Way to Think of a Graph: See What Happened to Various Inputs
2:25
Example
2:47
Graph as Location of Solution
4:20
A Way to See Solutions
4:36
Example
5:20
Which Way Should We Interpret?
7:13
Easiest to Think In Terms of How Inputs Are Mapped to Outputs
7:20
Sometimes It's Easier to Think In Terms of Solutions
8:39
Pay Attention to Axes
9:50
Axes Tell Where the Graph Is and What Scale It Has
10:09
Often, The Axes Will Be Square
10:14
Example
12:06
Arrows or No Arrows?
16:07
Will Not Use Arrows at the End of Our Graphs
17:13
Graph Stops Because It Hits the Edge of the Graphing Axes, Not Because the Function Stops
17:18
How to Graph
19:47
Plot Points
20:07
Connect with Curves
21:09
If You Connect with Straight Lines
21:44
Graphs of Functions are Smooth
22:21
More Points ⇒ More Accurate
23:38
Vertical Line Test
27:44
If a Vertical Line Could Intersect More Than One Point On a Graph, It Can Not Be the Graph of a Function
28:41
Every Point on a Graph Tells Us Where the x-Value Below is Mapped
30:07
Domain in Graphs
31:37
The Domain is the Set of All Inputs That a Function Can Accept
31:44
Be Aware That Our Function Probably Continues Past the Edge of Our 'Viewing Window'
33:19
Range in Graphs
33:53
Graphing Calculators: Check the Appendix!
36:55
Example 1
38:37
Example 2
45:19
Example 3
50:41
Example 4
53:28
Example 5
55:50
Properties of Functions

48m 49s

Intro
0:00
Introduction
0:05
Increasing Decreasing Constant
0:43
Looking at a Specific Graph
1:15
Increasing Interval
2:39
Constant Function
4:15
Decreasing Interval
5:10
Find Intervals by Looking at the Graph
5:32
Intervals Show x-values; Write in Parentheses
6:39
Maximum and Minimums
8:48
Relative (Local) Max/Min
10:20
Formal Definition of Relative Maximum
12:44
Formal Definition of Relative Minimum
13:05
Max/Min, More Terms
14:18
Definition of Extrema
15:01
Average Rate of Change
16:11
Drawing a Line for the Average Rate
16:48
Using the Slope of the Secant Line
17:36
Slope in Function Notation
18:45
Zeros/Roots/x-intercepts
19:45
What Zeros in a Function Mean
20:25
Even Functions
22:30
Odd Functions
24:36
Even/Odd Functions and Graphs
26:28
Example of an Even Function
27:12
Example of an Odd Function
28:03
Example 1
29:35
Example 2
33:07
Example 3
40:32
Example 4
42:34
Function Petting Zoo

29m 20s

Intro
0:00
Introduction
0:04
Don't Forget that Axes Matter!
1:44
The Constant Function
2:40
The Identity Function
3:44
The Square Function
4:40
The Cube Function
5:44
The Square Root Function
6:51
The Reciprocal Function
8:11
The Absolute Value Function
10:19
The Trigonometric Functions
11:56
f(x)=sin(x)
12:12
f(x)=cos(x)
12:24
Alternate Axes
12:40
The Exponential and Logarithmic Functions
13:35
Exponential Functions
13:44
Logarithmic Functions
14:24
Alternating Axes
15:17
Transformations and Compositions
16:08
Example 1
17:52
Example 2
18:33
Example 3
20:24
Example 4
26:07
Transformation of Functions

48m 35s

Intro
0:00
Introduction
0:04
Vertical Shift
1:12
Graphical Example
1:21
A Further Explanation
2:16
Vertical Stretch/Shrink
3:34
Graph Shrinks
3:46
Graph Stretches
3:51
A Further Explanation
5:07
Horizontal Shift
6:49
Moving the Graph to the Right
7:28
Moving the Graph to the Left
8:12
A Further Explanation
8:19
Understanding Movement on the x-axis
8:38
Horizontal Stretch/Shrink
12:59
Shrinking the Graph
13:40
Stretching the Graph
13:48
A Further Explanation
13:55
Understanding Stretches from the x-axis
14:12
Vertical Flip (aka Mirror)
16:55
Example Graph
17:07
Multiplying the Vertical Component by -1
17:18
Horizontal Flip (aka Mirror)
18:43
Example Graph
19:01
Multiplying the Horizontal Component by -1
19:54
Summary of Transformations
22:11
Stacking Transformations
24:46
Order Matters
25:20
Transformation Example
25:52
Example 1
29:21
Example 2
34:44
Example 3
38:10
Example 4
43:46
Composite Functions

33m 24s

Intro
0:00
Introduction
0:04
Arithmetic Combinations
0:40
Basic Operations
1:20
Definition of the Four Arithmetic Combinations
1:40
Composite Functions
2:53
The Function as a Machine
3:32
Function Compositions as Multiple Machines
3:59
Notation for Composite Functions
4:46
Two Formats
6:02
Another Visual Interpretation
7:17
How to Use Composite Functions
8:21
Example of on Function acting on Another
9:17
Example 1
11:03
Example 2
15:27
Example 3
21:11
Example 4
27:06
Piecewise Functions

51m 42s

Intro
0:00
Introduction
0:04
Analogies to a Piecewise Function
1:16
Different Potatoes
1:41
Factory Production
2:27
Notations for Piecewise Functions
3:39
Notation Examples from Analogies
6:11
Example of a Piecewise (with Table)
7:24
Example of a Non-Numerical Piecewise
11:35
Graphing Piecewise Functions
14:15
Graphing Piecewise Functions, Example
16:26
Continuous Functions
16:57
Statements of Continuity
19:30
Example of Continuous and Non-Continuous Graphs
20:05
Interesting Functions: the Step Function
22:00
Notation for the Step Function
22:40
How the Step Function Works
22:56
Graph of the Step Function
25:30
Example 1
26:22
Example 2
28:49
Example 3
36:50
Example 4
46:11
Inverse Functions

49m 37s

Intro
0:00
Introduction
0:04
Analogy by picture
1:10
How to Denote the inverse
1:40
What Comes out of the Inverse
1:52
Requirement for Reversing
2:02
The Basketball Factory
2:12
The Importance of Information
2:45
One-to-One
4:04
Requirement for Reversibility
4:21
When a Function has an Inverse
4:43
One-to-One
5:13
Not One-to-One
5:50
Not a Function
6:19
Horizontal Line Test
7:01
How to the test Works
7:12
One-to-One
8:12
Not One-to-One
8:45
Definition: Inverse Function
9:12
Formal Definition
9:21
Caution to Students
10:02
Domain and Range
11:12
Finding the Range of the Function Inverse
11:56
Finding the Domain of the Function Inverse
12:11
Inverse of an Inverse
13:09
Its just x!
13:26
Proof
14:03
Graphical Interpretation
17:07
Horizontal Line Test
17:20
Graph of the Inverse
18:04
Swapping Inputs and Outputs to Draw Inverses
19:02
How to Find the Inverse
21:03
What We Are Looking For
21:21
Reversing the Function
21:38
A Method to Find Inverses
22:33
Check Function is One-to-One
23:04
Swap f(x) for y
23:25
Interchange x and y
23:41
Solve for y
24:12
Replace y with the inverse
24:40
Some Comments
25:01
Keeping Step 2 and 3 Straight
25:44
Switching to Inverse
26:12
Checking Inverses
28:52
How to Check an Inverse
29:06
Quick Example of How to Check
29:56
Example 1
31:48
Example 2
34:56
Example 3
39:29
Example 4
46:19
Variation Direct and Inverse

28m 49s

Intro
0:00
Introduction
0:06
Direct Variation
1:14
Same Direction
1:21
Common Example: Groceries
1:56
Different Ways to Say that Two Things Vary Directly
2:28
Basic Equation for Direct Variation
2:55
Inverse Variation
3:40
Opposite Direction
3:50
Common Example: Gravity
4:53
Different Ways to Say that Two Things Vary Indirectly
5:48
Basic Equation for Indirect Variation
6:33
Joint Variation
7:27
Equation for Joint Variation
7:53
Explanation of the Constant
8:48
Combined Variation
9:35
Gas Law as a Combination
9:44
Single Constant
10:33
Example 1
10:49
Example 2
13:34
Example 3
15:39
Example 4
19:48
Section 3: Polynomials
Intro to Polynomials

38m 41s

Intro
0:00
Introduction
0:04
Definition of a Polynomial
1:04
Starting Integer
2:06
Structure of a Polynomial
2:49
The a Constants
3:34
Polynomial Function
5:13
Polynomial Equation
5:23
Polynomials with Different Variables
5:36
Degree
6:23
Informal Definition
6:31
Find the Largest Exponent Variable
6:44
Quick Examples
7:36
Special Names for Polynomials
8:59
Based on the Degree
9:23
Based on the Number of Terms
10:12
Distributive Property (aka 'FOIL')
11:37
Basic Distributive Property
12:21
Distributing Two Binomials
12:55
Longer Parentheses
15:12
Reverse: Factoring
17:26
Long-Term Behavior of Polynomials
17:48
Examples
18:13
Controlling Term--Term with the Largest Exponent
19:33
Positive and Negative Coefficients on the Controlling Term
20:21
Leading Coefficient Test
22:07
Even Degree, Positive Coefficient
22:13
Even Degree, Negative Coefficient
22:39
Odd Degree, Positive Coefficient
23:09
Odd Degree, Negative Coefficient
23:27
Example 1
25:11
Example 2
27:16
Example 3
31:16
Example 4
34:41
Roots (Zeros) of Polynomials

41m 7s

Intro
0:00
Introduction
0:05
Roots in Graphs
1:17
The x-intercepts
1:33
How to Remember What 'Roots' Are
1:50
Naïve Attempts
2:31
Isolating Variables
2:45
Failures of Isolating Variables
3:30
Missing Solutions
4:59
Factoring: How to Find Roots
6:28
How Factoring Works
6:36
Why Factoring Works
7:20
Steps to Finding Polynomial Roots
9:21
Factoring: How to Find Roots CAUTION
10:08
Factoring is Not Easy
11:32
Factoring Quadratics
13:08
Quadratic Trinomials
13:21
Form of Factored Binomials
13:38
Factoring Examples
14:40
Factoring Quadratics, Check Your Work
16:58
Factoring Higher Degree Polynomials
18:19
Factoring a Cubic
18:32
Factoring a Quadratic
19:04
Factoring: Roots Imply Factors
19:54
Where a Root is, A Factor Is
20:01
How to Use Known Roots to Make Factoring Easier
20:35
Not all Polynomials Can be Factored
22:30
Irreducible Polynomials
23:27
Complex Numbers Help
23:55
Max Number of Roots/Factors
24:57
Limit to Number of Roots Equal to the Degree
25:18
Why there is a Limit
25:25
Max Number of Peaks/Valleys
26:39
Shape Information from Degree
26:46
Example Graph
26:54
Max, But Not Required
28:00
Example 1
28:37
Example 2
31:21
Example 3
36:12
Example 4
38:40
Completing the Square and the Quadratic Formula

39m 43s

Intro
0:00
Introduction
0:05
Square Roots and Equations
0:51
Taking the Square Root to Find the Value of x
0:55
Getting the Positive and Negative Answers
1:05
Completing the Square: Motivation
2:04
Polynomials that are Easy to Solve
2:20
Making Complex Polynomials Easy to Solve
3:03
Steps to Completing the Square
4:30
Completing the Square: Method
7:22
Move C over
7:35
Divide by A
7:44
Find r
7:59
Add to Both Sides to Complete the Square
8:49
Solving Quadratics with Ease
9:56
The Quadratic Formula
11:38
Derivation
11:43
Final Form
12:23
Follow Format to Use Formula
13:38
How Many Roots?
14:53
The Discriminant
15:47
What the Discriminant Tells Us: How Many Roots
15:58
How the Discriminant Works
16:30
Example 1: Complete the Square
18:24
Example 2: Solve the Quadratic
22:00
Example 3: Solve for Zeroes
25:28
Example 4: Using the Quadratic Formula
30:52
Properties of Quadratic Functions

45m 34s

Intro
0:00
Introduction
0:05
Parabolas
0:35
Examples of Different Parabolas
1:06
Axis of Symmetry and Vertex
1:28
Drawing an Axis of Symmetry
1:51
Placing the Vertex
2:28
Looking at the Axis of Symmetry and Vertex for other Parabolas
3:09
Transformations
4:18
Reviewing Transformation Rules
6:28
Note the Different Horizontal Shift Form
7:45
An Alternate Form to Quadratics
8:54
The Constants: k, h, a
9:05
Transformations Formed
10:01
Analyzing Different Parabolas
10:10
Switching Forms by Completing the Square
11:43
Vertex of a Parabola
16:30
Vertex at (h, k)
16:47
Vertex in Terms of a, b, and c Coefficients
17:28
Minimum/Maximum at Vertex
18:19
When a is Positive
18:25
When a is Negative
18:52
Axis of Symmetry
19:54
Incredibly Minor Note on Grammar
20:52
Example 1
21:48
Example 2
26:35
Example 3
28:55
Example 4
31:40
Intermediate Value Theorem and Polynomial Division

46m 8s

Intro
0:00
Introduction
0:05
Reminder: Roots Imply Factors
1:32
The Intermediate Value Theorem
3:41
The Basis: U between a and b
4:11
U is on the Function
4:52
Intermediate Value Theorem, Proof Sketch
5:51
If Not True, the Graph Would Have to Jump
5:58
But Graph is Defined as Continuous
6:43
Finding Roots with the Intermediate Value Theorem
7:01
Picking a and b to be of Different Signs
7:10
Must Be at Least One Root
7:46
Dividing a Polynomial
8:16
Using Roots and Division to Factor
8:38
Long Division Refresher
9:08
The Division Algorithm
12:18
How It Works to Divide Polynomials
12:37
The Parts of the Equation
13:24
Rewriting the Equation
14:47
Polynomial Long Division
16:20
Polynomial Long Division In Action
16:29
One Step at a Time
20:51
Synthetic Division
22:46
Setup
23:11
Synthetic Division, Example
24:44
Which Method Should We Use
26:39
Advantages of Synthetic Method
26:49
Advantages of Long Division
27:13
Example 1
29:24
Example 2
31:27
Example 3
36:22
Example 4
40:55
Complex Numbers

45m 36s

Intro
0:00
Introduction
0:04
A Wacky Idea
1:02
The Definition of the Imaginary Number
1:22
How it Helps Solve Equations
2:20
Square Roots and Imaginary Numbers
3:15
Complex Numbers
5:00
Real Part and Imaginary Part
5:20
When Two Complex Numbers are Equal
6:10
Addition and Subtraction
6:40
Deal with Real and Imaginary Parts Separately
7:36
Two Quick Examples
7:54
Multiplication
9:07
FOIL Expansion
9:14
Note What Happens to the Square of the Imaginary Number
9:41
Two Quick Examples
10:22
Division
11:27
Complex Conjugates
13:37
Getting Rid of i
14:08
How to Denote the Conjugate
14:48
Division through Complex Conjugates
16:11
Multiply by the Conjugate of the Denominator
16:28
Example
17:46
Factoring So-Called 'Irreducible' Quadratics
19:24
Revisiting the Quadratic Formula
20:12
Conjugate Pairs
20:37
But Are the Complex Numbers 'Real'?
21:27
What Makes a Number Legitimate
25:38
Where Complex Numbers are Used
27:20
Still, We Won't See Much of C
29:05
Example 1
30:30
Example 2
33:15
Example 3
38:12
Example 4
42:07
Fundamental Theorem of Algebra

19m 9s

Intro
0:00
Introduction
0:05
Idea: Hidden Roots
1:16
Roots in Complex Form
1:42
All Polynomials Have Roots
2:08
Fundamental Theorem of Algebra
2:21
Where Are All the Imaginary Roots, Then?
3:17
All Roots are Complex
3:45
Real Numbers are a Subset of Complex Numbers
3:59
The n Roots Theorem
5:01
For Any Polynomial, Its Degree is Equal to the Number of Roots
5:11
Equivalent Statement
5:24
Comments: Multiplicity
6:29
Non-Distinct Roots
6:59
Denoting Multiplicity
7:20
Comments: Complex Numbers Necessary
7:41
Comments: Complex Coefficients Allowed
8:55
Comments: Existence Theorem
9:59
Proof Sketch of n Roots Theorem
10:45
First Root
11:36
Second Root
13:23
Continuation to Find all Roots
16:00
Section 4: Rational Functions
Rational Functions and Vertical Asymptotes

33m 22s

Intro
0:00
Introduction
0:05
Definition of a Rational Function
1:20
Examples of Rational Functions
2:30
Why They are Called 'Rational'
2:47
Domain of a Rational Function
3:15
Undefined at Denominator Zeros
3:25
Otherwise all Reals
4:16
Investigating a Fundamental Function
4:50
The Domain of the Function
5:04
What Occurs at the Zeroes of the Denominator
5:20
Idea of a Vertical Asymptote
6:23
What's Going On?
6:58
Approaching x=0 from the left
7:32
Approaching x=0 from the right
8:34
Dividing by Very Small Numbers Results in Very Large Numbers
9:31
Definition of a Vertical Asymptote
10:05
Vertical Asymptotes and Graphs
11:15
Drawing Asymptotes by Using a Dashed Line
11:27
The Graph Can Never Touch Its Undefined Point
12:00
Not All Zeros Give Asymptotes
13:02
Special Cases: When Numerator and Denominator Go to Zero at the Same Time
14:58
Cancel out Common Factors
15:49
How to Find Vertical Asymptotes
16:10
Figure out What Values Are Not in the Domain of x
16:24
Determine if the Numerator and Denominator Share Common Factors and Cancel
16:45
Find Denominator Roots
17:33
Note if Asymptote Approaches Negative or Positive Infinity
18:06
Example 1
18:57
Example 2
21:26
Example 3
23:04
Example 4
30:01
Horizontal Asymptotes

34m 16s

Intro
0:00
Introduction
0:05
Investigating a Fundamental Function
0:53
What Happens as x Grows Large
1:00
Different View
1:12
Idea of a Horizontal Asymptote
1:36
What's Going On?
2:24
What Happens as x Grows to a Large Negative Number
2:49
What Happens as x Grows to a Large Number
3:30
Dividing by Very Large Numbers Results in Very Small Numbers
3:52
Example Function
4:41
Definition of a Vertical Asymptote
8:09
Expanding the Idea
9:03
What's Going On?
9:48
What Happens to the Function in the Long Run?
9:51
Rewriting the Function
10:13
Definition of a Slant Asymptote
12:09
Symbolical Definition
12:30
Informal Definition
12:45
Beyond Slant Asymptotes
13:03
Not Going Beyond Slant Asymptotes
14:39
Horizontal/Slant Asymptotes and Graphs
15:43
How to Find Horizontal and Slant Asymptotes
16:52
How to Find Horizontal Asymptotes
17:12
Expand the Given Polynomials
17:18
Compare the Degrees of the Numerator and Denominator
17:40
How to Find Slant Asymptotes
20:05
Slant Asymptotes Exist When n+m=1
20:08
Use Polynomial Division
20:24
Example 1
24:32
Example 2
25:53
Example 3
26:55
Example 4
29:22
Graphing Asymptotes in a Nutshell

49m 7s

Intro
0:00
Introduction
0:05
A Process for Graphing
1:22
1. Factor Numerator and Denominator
1:50
2. Find Domain
2:53
3. Simplifying the Function
3:59
4. Find Vertical Asymptotes
4:59
5. Find Horizontal/Slant Asymptotes
5:24
6. Find Intercepts
7:35
7. Draw Graph (Find Points as Necessary)
9:21
Draw Graph Example
11:21
Vertical Asymptote
11:41
Horizontal Asymptote
11:50
Other Graphing
12:16
Test Intervals
15:08
Example 1
17:57
Example 2
23:01
Example 3
29:02
Example 4
33:37
Partial Fractions

44m 56s

Intro
0:00
Introduction: Idea
0:04
Introduction: Prerequisites and Uses
1:57
Proper vs. Improper Polynomial Fractions
3:11
Possible Things in the Denominator
4:38
Linear Factors
6:16
Example of Linear Factors
7:03
Multiple Linear Factors
7:48
Irreducible Quadratic Factors
8:25
Example of Quadratic Factors
9:26
Multiple Quadratic Factors
9:49
Mixing Factor Types
10:28
Figuring Out the Numerator
11:10
How to Solve for the Constants
11:30
Quick Example
11:40
Example 1
14:29
Example 2
18:35
Example 3
20:33
Example 4
28:51
Section 5: Exponential & Logarithmic Functions
Understanding Exponents

35m 17s

Intro
0:00
Introduction
0:05
Fundamental Idea
1:46
Expanding the Idea
2:28
Multiplication of the Same Base
2:40
Exponents acting on Exponents
3:45
Different Bases with the Same Exponent
4:31
To the Zero
5:35
To the First
5:45
Fundamental Rule with the Zero Power
6:35
To the Negative
7:45
Any Number to a Negative Power
8:14
A Fraction to a Negative Power
9:58
Division with Exponential Terms
10:41
To the Fraction
11:33
Square Root
11:58
Any Root
12:59
Summary of Rules
14:38
To the Irrational
17:21
Example 1
20:34
Example 2
23:42
Example 3
27:44
Example 4
31:44
Example 5
33:15
Exponential Functions

47m 4s

Intro
0:00
Introduction
0:05
Definition of an Exponential Function
0:48
Definition of the Base
1:02
Restrictions on the Base
1:16
Computing Exponential Functions
2:29
Harder Computations
3:10
When to Use a Calculator
3:21
Graphing Exponential Functions: a>1
6:02
Three Examples
6:13
What to Notice on the Graph
7:44
A Story
8:27
Story Diagram
9:15
Increasing Exponentials
11:29
Story Morals
14:40
Application: Compound Interest
15:15
Compounding Year after Year
16:01
Function for Compounding Interest
16:51
A Special Number: e
20:55
Expression for e
21:28
Where e stabilizes
21:55
Application: Continuously Compounded Interest
24:07
Equation for Continuous Compounding
24:22
Exponential Decay 0<a<1
25:50
Three Examples
26:11
Why they 'lose' value
26:54
Example 1
27:47
Example 2
33:11
Example 3
36:34
Example 4
41:28
Introduction to Logarithms

40m 31s

Intro
0:00
Introduction
0:04
Definition of a Logarithm, Base 2
0:51
Log 2 Defined
0:55
Examples
2:28
Definition of a Logarithm, General
3:23
Examples of Logarithms
5:15
Problems with Unusual Bases
7:38
Shorthand Notation: ln and log
9:44
base e as ln
10:01
base 10 as log
10:34
Calculating Logarithms
11:01
using a calculator
11:34
issues with other bases
11:58
Graphs of Logarithms
13:21
Three Examples
13:29
Slow Growth
15:19
Logarithms as Inverse of Exponentiation
16:02
Using Base 2
16:05
General Case
17:10
Looking More Closely at Logarithm Graphs
19:16
The Domain of Logarithms
20:41
Thinking about Logs like Inverses
21:08
The Alternate
24:00
Example 1
25:59
Example 2
30:03
Example 3
32:49
Example 4
37:34
Properties of Logarithms

42m 33s

Intro
0:00
Introduction
0:04
Basic Properties
1:12
Inverse--log(exp)
1:43
A Key Idea
2:44
What We Get through Exponentiation
3:18
B Always Exists
4:50
Inverse--exp(log)
5:53
Logarithm of a Power
7:44
Logarithm of a Product
10:07
Logarithm of a Quotient
13:48
Caution! There Is No Rule for loga(M+N)
16:12
Summary of Properties
17:42
Change of Base--Motivation
20:17
No Calculator Button
20:59
A Specific Example
21:45
Simplifying
23:45
Change of Base--Formula
24:14
Example 1
25:47
Example 2
29:08
Example 3
31:14
Example 4
34:13
Solving Exponential and Logarithmic Equations

34m 10s

Intro
0:00
Introduction
0:05
One to One Property
1:09
Exponential
1:26
Logarithmic
1:44
Specific Considerations
2:02
One-to-One Property
3:30
Solving by One-to-One
4:11
Inverse Property
6:09
Solving by Inverses
7:25
Dealing with Equations
7:50
Example of Taking an Exponent or Logarithm of an Equation
9:07
A Useful Property
11:57
Bring Down Exponents
12:01
Try to Simplify
13:20
Extraneous Solutions
13:45
Example 1
16:37
Example 2
19:39
Example 3
21:37
Example 4
26:45
Example 5
29:37
Application of Exponential and Logarithmic Functions

48m 46s

Intro
0:00
Introduction
0:06
Applications of Exponential Functions
1:07
A Secret!
2:17
Natural Exponential Growth Model
3:07
Figure out r
3:34
A Secret!--Why Does It Work?
4:44
e to the r Morphs
4:57
Example
5:06
Applications of Logarithmic Functions
8:32
Examples
8:43
What Logarithms are Useful For
9:53
Example 1
11:29
Example 2
15:30
Example 3
26:22
Example 4
32:05
Example 5
39:19
Section 6: Trigonometric Functions
Angles

39m 5s

Intro
0:00
Degrees
0:22
Circle is 360 Degrees
0:48
Splitting a Circle
1:13
Radians
2:08
Circle is 2 Pi Radians
2:31
One Radian
2:52
Half-Circle and Right Angle
4:00
Converting Between Degrees and Radians
6:24
Formulas for Degrees and Radians
6:52
Coterminal, Complementary, Supplementary Angles
7:23
Coterminal Angles
7:30
Complementary Angles
9:40
Supplementary Angles
10:08
Example 1: Dividing a Circle
10:38
Example 2: Converting Between Degrees and Radians
11:56
Example 3: Quadrants and Coterminal Angles
14:18
Extra Example 1: Common Angle Conversions
-1
Extra Example 2: Quadrants and Coterminal Angles
-2
Sine and Cosine Functions

43m 16s

Intro
0:00
Sine and Cosine
0:15
Unit Circle
0:22
Coordinates on Unit Circle
1:03
Right Triangles
1:52
Adjacent, Opposite, Hypotenuse
2:25
Master Right Triangle Formula: SOHCAHTOA
2:48
Odd Functions, Even Functions
4:40
Example: Odd Function
4:56
Example: Even Function
7:30
Example 1: Sine and Cosine
10:27
Example 2: Graphing Sine and Cosine Functions
14:39
Example 3: Right Triangle
21:40
Example 4: Odd, Even, or Neither
26:01
Extra Example 1: Right Triangle
-1
Extra Example 2: Graphing Sine and Cosine Functions
-2
Sine and Cosine Values of Special Angles

33m 5s

Intro
0:00
45-45-90 Triangle and 30-60-90 Triangle
0:08
45-45-90 Triangle
0:21
30-60-90 Triangle
2:06
Mnemonic: All Students Take Calculus (ASTC)
5:21
Using the Unit Circle
5:59
New Angles
6:21
Other Quadrants
9:43
Mnemonic: All Students Take Calculus
10:13
Example 1: Convert, Quadrant, Sine/Cosine
13:11
Example 2: Convert, Quadrant, Sine/Cosine
16:48
Example 3: All Angles and Quadrants
20:21
Extra Example 1: Convert, Quadrant, Sine/Cosine
-1
Extra Example 2: All Angles and Quadrants
-2
Modified Sine Waves: Asin(Bx+C)+D and Acos(Bx+C)+D

52m 3s

Intro
0:00
Amplitude and Period of a Sine Wave
0:38
Sine Wave Graph
0:58
Amplitude: Distance from Middle to Peak
1:18
Peak: Distance from Peak to Peak
2:41
Phase Shift and Vertical Shift
4:13
Phase Shift: Distance Shifted Horizontally
4:16
Vertical Shift: Distance Shifted Vertically
6:48
Example 1: Amplitude/Period/Phase and Vertical Shift
8:04
Example 2: Amplitude/Period/Phase and Vertical Shift
17:39
Example 3: Find Sine Wave Given Attributes
25:23
Extra Example 1: Amplitude/Period/Phase and Vertical Shift
-1
Extra Example 2: Find Cosine Wave Given Attributes
-2
Tangent and Cotangent Functions

36m 4s

Intro
0:00
Tangent and Cotangent Definitions
0:21
Tangent Definition
0:25
Cotangent Definition
0:47
Master Formula: SOHCAHTOA
1:01
Mnemonic
1:16
Tangent and Cotangent Values
2:29
Remember Common Values of Sine and Cosine
2:46
90 Degrees Undefined
4:36
Slope and Menmonic: ASTC
5:47
Uses of Tangent
5:54
Example: Tangent of Angle is Slope
6:09
Sign of Tangent in Quadrants
7:49
Example 1: Graph Tangent and Cotangent Functions
10:42
Example 2: Tangent and Cotangent of Angles
16:09
Example 3: Odd, Even, or Neither
18:56
Extra Example 1: Tangent and Cotangent of Angles
-1
Extra Example 2: Tangent and Cotangent of Angles
-2
Secant and Cosecant Functions

27m 18s

Intro
0:00
Secant and Cosecant Definitions
0:17
Secant Definition
0:18
Cosecant Definition
0:33
Example 1: Graph Secant Function
0:48
Example 2: Values of Secant and Cosecant
6:49
Example 3: Odd, Even, or Neither
12:49
Extra Example 1: Graph of Cosecant Function
-1
Extra Example 2: Values of Secant and Cosecant
-2
Inverse Trigonometric Functions

32m 58s

Intro
0:00
Arcsine Function
0:24
Restrictions between -1 and 1
0:43
Arcsine Notation
1:26
Arccosine Function
3:07
Restrictions between -1 and 1
3:36
Cosine Notation
3:53
Arctangent Function
4:30
Between -Pi/2 and Pi/2
4:44
Tangent Notation
5:02
Example 1: Domain/Range/Graph of Arcsine
5:45
Example 2: Arcsin/Arccos/Arctan Values
10:46
Example 3: Domain/Range/Graph of Arctangent
17:14
Extra Example 1: Domain/Range/Graph of Arccosine
-1
Extra Example 2: Arcsin/Arccos/Arctan Values
-2
Computations of Inverse Trigonometric Functions

31m 8s

Intro
0:00
Inverse Trigonometric Function Domains and Ranges
0:31
Arcsine
0:41
Arccosine
1:14
Arctangent
1:41
Example 1: Arcsines of Common Values
2:44
Example 2: Odd, Even, or Neither
5:57
Example 3: Arccosines of Common Values
12:24
Extra Example 1: Arctangents of Common Values
-1
Extra Example 2: Arcsin/Arccos/Arctan Values
-2
Section 7: Trigonometric Identities
Pythagorean Identity

19m 11s

Intro
0:00
Pythagorean Identity
0:17
Pythagorean Triangle
0:27
Pythagorean Identity
0:45
Example 1: Use Pythagorean Theorem to Prove Pythagorean Identity
1:14
Example 2: Find Angle Given Cosine and Quadrant
4:18
Example 3: Verify Trigonometric Identity
8:00
Extra Example 1: Use Pythagorean Identity to Prove Pythagorean Theorem
-1
Extra Example 2: Find Angle Given Cosine and Quadrant
-2
Identity Tan(squared)x+1=Sec(squared)x

23m 16s

Intro
0:00
Main Formulas
0:19
Companion to Pythagorean Identity
0:27
For Cotangents and Cosecants
0:52
How to Remember
0:58
Example 1: Prove the Identity
1:40
Example 2: Given Tan Find Sec
3:42
Example 3: Prove the Identity
7:45
Extra Example 1: Prove the Identity
-1
Extra Example 2: Given Sec Find Tan
-2
Addition and Subtraction Formulas

52m 52s

Intro
0:00
Addition and Subtraction Formulas
0:09
How to Remember
0:48
Cofunction Identities
1:31
How to Remember Graphically
1:44
Where to Use Cofunction Identities
2:52
Example 1: Derive the Formula for cos(A-B)
3:08
Example 2: Use Addition and Subtraction Formulas
16:03
Example 3: Use Addition and Subtraction Formulas to Prove Identity
25:11
Extra Example 1: Use cos(A-B) and Cofunction Identities
-1
Extra Example 2: Convert to Radians and use Formulas
-2
Double Angle Formulas

29m 5s

Intro
0:00
Main Formula
0:07
How to Remember from Addition Formula
0:18
Two Other Forms
1:35
Example 1: Find Sine and Cosine of Angle using Double Angle
3:16
Example 2: Prove Trigonometric Identity using Double Angle
9:37
Example 3: Use Addition and Subtraction Formulas
12:38
Extra Example 1: Find Sine and Cosine of Angle using Double Angle
-1
Extra Example 2: Prove Trigonometric Identity using Double Angle
-2
Half-Angle Formulas

43m 55s

Intro
0:00
Main Formulas
0:09
Confusing Part
0:34
Example 1: Find Sine and Cosine of Angle using Half-Angle
0:54
Example 2: Prove Trigonometric Identity using Half-Angle
11:51
Example 3: Prove the Half-Angle Formula for Tangents
18:39
Extra Example 1: Find Sine and Cosine of Angle using Half-Angle
-1
Extra Example 2: Prove Trigonometric Identity using Half-Angle
-2
Section 8: Applications of Trigonometry
Trigonometry in Right Angles

25m 43s

Intro
0:00
Master Formula for Right Angles
0:11
SOHCAHTOA
0:15
Only for Right Triangles
1:26
Example 1: Find All Angles in a Triangle
2:19
Example 2: Find Lengths of All Sides of Triangle
7:39
Example 3: Find All Angles in a Triangle
11:00
Extra Example 1: Find All Angles in a Triangle
-1
Extra Example 2: Find Lengths of All Sides of Triangle
-2
Law of Sines

56m 40s

Intro
0:00
Law of Sines Formula
0:18
SOHCAHTOA
0:27
Any Triangle
0:59
Graphical Representation
1:25
Solving Triangle Completely
2:37
When to Use Law of Sines
2:55
ASA, SAA, SSA, AAA
2:59
SAS, SSS for Law of Cosines
7:11
Example 1: How Many Triangles Satisfy Conditions, Solve Completely
8:44
Example 2: How Many Triangles Satisfy Conditions, Solve Completely
15:30
Example 3: How Many Triangles Satisfy Conditions, Solve Completely
28:32
Extra Example 1: How Many Triangles Satisfy Conditions, Solve Completely
-1
Extra Example 2: How Many Triangles Satisfy Conditions, Solve Completely
-2
Law of Cosines

49m 5s

Intro
0:00
Law of Cosines Formula
0:23
Graphical Representation
0:34
Relates Sides to Angles
1:00
Any Triangle
1:20
Generalization of Pythagorean Theorem
1:32
When to Use Law of Cosines
2:26
SAS, SSS
2:30
Heron's Formula
4:49
Semiperimeter S
5:11
Example 1: How Many Triangles Satisfy Conditions, Solve Completely
5:53
Example 2: How Many Triangles Satisfy Conditions, Solve Completely
15:19
Example 3: Find Area of a Triangle Given All Side Lengths
26:33
Extra Example 1: How Many Triangles Satisfy Conditions, Solve Completely
-1
Extra Example 2: Length of Third Side and Area of Triangle
-2
Finding the Area of a Triangle

27m 37s

Intro
0:00
Master Right Triangle Formula and Law of Cosines
0:19
SOHCAHTOA
0:27
Law of Cosines
1:23
Heron's Formula
2:22
Semiperimeter S
2:37
Example 1: Area of Triangle with Two Sides and One Angle
3:12
Example 2: Area of Triangle with Three Sides
6:11
Example 3: Area of Triangle with Three Sides, No Heron's Formula
8:50
Extra Example 1: Area of Triangle with Two Sides and One Angle
-1
Extra Example 2: Area of Triangle with Two Sides and One Angle
-2
Word Problems and Applications of Trigonometry

34m 25s

Intro
0:00
Formulas to Remember
0:11
SOHCAHTOA
0:15
Law of Sines
0:55
Law of Cosines
1:48
Heron's Formula
2:46
Example 1: Telephone Pole Height
4:01
Example 2: Bridge Length
7:48
Example 3: Area of Triangular Field
14:20
Extra Example 1: Kite Height
-1
Extra Example 2: Roads to a Town
-2
Section 9: Systems of Equations and Inequalities
Systems of Linear Equations

55m 40s

Intro
0:00
Introduction
0:04
Graphs as Location of 'True'
1:49
All Locations that Make the Function True
2:25
Understand the Relationship Between Solutions and the Graph
3:43
Systems as Graphs
4:07
Equations as Lines
4:20
Intersection Point
5:19
Three Possibilities for Solutions
6:17
Independent
6:24
Inconsistent
6:36
Dependent
7:06
Solving by Substitution
8:37
Solve for One Variable
9:07
Substitute into the Second Equation
9:34
Solve for Both Variables
10:12
What If a System is Inconsistent or Dependent?
11:08
No Solutions
11:25
Infinite Solutions
12:30
Solving by Elimination
13:56
Example
14:22
Determining the Number of Solutions
16:30
Why Elimination Makes Sense
17:25
Solving by Graphing Calculator
19:59
Systems with More than Two Variables
23:22
Example 1
25:49
Example 2
30:22
Example 3
34:11
Example 4
38:55
Example 5
46:01
(Non-) Example 6
53:37
Systems of Linear Inequalities

1h 13s

Intro
0:00
Introduction
0:04
Inequality Refresher-Solutions
0:46
Equation Solutions vs. Inequality Solutions
1:02
Essentially a Wide Variety of Answers
1:35
Refresher--Negative Multiplication Flips
1:43
Refresher--Negative Flips: Why?
3:19
Multiplication by a Negative
3:43
The Relationship Flips
3:55
Refresher--Stick to Basic Operations
4:34
Linear Equations in Two Variables
6:50
Graphing Linear Inequalities
8:28
Why It Includes a Whole Section
8:43
How to Show The Difference Between Strict and Not Strict Inequalities
10:08
Dashed Line--Not Solutions
11:10
Solid Line--Are Solutions
11:24
Test Points for Shading
11:42
Example of Using a Point
12:41
Drawing Shading from the Point
13:14
Graphing a System
14:53
Set of Solutions is the Overlap
15:17
Example
15:22
Solutions are Best Found Through Graphing
18:05
Linear Programming-Idea
19:52
Use a Linear Objective Function
20:15
Variables in Objective Function have Constraints
21:24
Linear Programming-Method
22:09
Rearrange Equations
22:21
Graph
22:49
Critical Solution is at the Vertex of the Overlap
23:40
Try Each Vertice
24:35
Example 1
24:58
Example 2
28:57
Example 3
33:48
Example 4
43:10
Nonlinear Systems

41m 1s

Intro
0:00
Introduction
0:06
Substitution
1:12
Example
1:22
Elimination
3:46
Example
3:56
Elimination is Less Useful for Nonlinear Systems
4:56
Graphing
5:56
Using a Graphing Calculator
6:44
Number of Solutions
8:44
Systems of Nonlinear Inequalities
10:02
Graph Each Inequality
10:06
Dashed and/or Solid
10:18
Shade Appropriately
11:14
Example 1
13:24
Example 2
15:50
Example 3
22:02
Example 4
29:06
Example 4, cont.
33:40
Section 10: Vectors and Matrices
Vectors

1h 9m 31s

Intro
0:00
Introduction
0:10
Magnitude of the Force
0:22
Direction of the Force
0:48
Vector
0:52
Idea of a Vector
1:30
How Vectors are Denoted
2:00
Component Form
3:20
Angle Brackets and Parentheses
3:50
Magnitude/Length
4:26
Denoting the Magnitude of a Vector
5:16
Direction/Angle
7:52
Always Draw a Picture
8:50
Component Form from Magnitude & Angle
10:10
Scaling by Scalars
14:06
Unit Vectors
16:26
Combining Vectors - Algebraically
18:10
Combining Vectors - Geometrically
19:54
Resultant Vector
20:46
Alternate Component Form: i, j
21:16
The Zero Vector
23:18
Properties of Vectors
24:20
No Multiplication (Between Vectors)
28:30
Dot Product
29:40
Motion in a Medium
30:10
Fish in an Aquarium Example
31:38
More Than Two Dimensions
33:12
More Than Two Dimensions - Magnitude
34:18
Example 1
35:26
Example 2
38:10
Example 3
45:48
Example 4
50:40
Example 4, cont.
56:07
Example 5
1:01:32
Dot Product & Cross Product

35m 20s

Intro
0:00
Introduction
0:08
Dot Product - Definition
0:42
Dot Product Results in a Scalar, Not a Vector
2:10
Example in Two Dimensions
2:34
Angle and the Dot Product
2:58
The Dot Product of Two Vectors is Deeply Related to the Angle Between the Two Vectors
2:59
Proof of Dot Product Formula
4:14
Won't Directly Help Us Better Understand Vectors
4:18
Dot Product - Geometric Interpretation
4:58
We Can Interpret the Dot Product as a Measure of How Long and How Parallel Two Vectors Are
7:26
Dot Product - Perpendicular Vectors
8:24
If the Dot Product of Two Vectors is 0, We Know They are Perpendicular to Each Other
8:54
Cross Product - Definition
11:08
Cross Product Only Works in Three Dimensions
11:09
Cross Product - A Mnemonic
12:16
The Determinant of a 3 x 3 Matrix and Standard Unit Vectors
12:17
Cross Product - Geometric Interpretations
14:30
The Right-Hand Rule
15:17
Cross Product - Geometric Interpretations Cont.
17:00
Example 1
18:40
Example 2
22:50
Example 3
24:04
Example 4
26:20
Bonus Round
29:18
Proof: Dot Product Formula
29:24
Proof: Dot Product Formula, cont.
30:38
Matrices

54m 7s

Intro
0:00
Introduction
0:08
Definition of a Matrix
3:02
Size or Dimension
3:58
Square Matrix
4:42
Denoted by Capital Letters
4:56
When are Two Matrices Equal?
5:04
Examples of Matrices
6:44
Rows x Columns
6:46
Talking About Specific Entries
7:48
We Use Capitals to Denote a Matrix and Lower Case to Denotes Its Entries
8:32
Using Entries to Talk About Matrices
10:08
Scalar Multiplication
11:26
Scalar = Real Number
11:34
Example
12:36
Matrix Addition
13:08
Example
14:22
Matrix Multiplication
15:00
Example
18:52
Matrix Multiplication, cont.
19:58
Matrix Multiplication and Order (Size)
25:26
Make Sure Their Orders are Compatible
25:27
Matrix Multiplication is NOT Commutative
28:20
Example
30:08
Special Matrices - Zero Matrix (0)
32:48
Zero Matrix Has 0 for All of its Entries
32:49
Special Matrices - Identity Matrix (I)
34:14
Identity Matrix is a Square Matrix That Has 1 for All Its Entries on the Main Diagonal and 0 for All Other Entries
34:15
Example 1
36:16
Example 2
40:00
Example 3
44:54
Example 4
50:08
Determinants & Inverses of Matrices

47m 12s

Intro
0:00
Introduction
0:06
Not All Matrices Are Invertible
1:30
What Must a Matrix Have to Be Invertible?
2:08
Determinant
2:32
The Determinant is a Real Number Associated With a Square Matrix
2:38
If the Determinant of a Matrix is Nonzero, the Matrix is Invertible
3:40
Determinant of a 2 x 2 Matrix
4:34
Think in Terms of Diagonals
5:12
Minors and Cofactors - Minors
6:24
Example
6:46
Minors and Cofactors - Cofactors
8:00
Cofactor is Closely Based on the Minor
8:01
Alternating Sign Pattern
9:04
Determinant of Larger Matrices
10:56
Example
13:00
Alternative Method for 3x3 Matrices
16:46
Not Recommended
16:48
Inverse of a 2 x 2 Matrix
19:02
Inverse of Larger Matrices
20:00
Using Inverse Matrices
21:06
When Multiplied Together, They Create the Identity Matrix
21:24
Example 1
23:45
Example 2
27:21
Example 3
32:49
Example 4
36:27
Finding the Inverse of Larger Matrices
41:59
General Inverse Method - Step 1
43:25
General Inverse Method - Step 2
43:27
General Inverse Method - Step 2, cont.
43:27
General Inverse Method - Step 3
45:15
Using Matrices to Solve Systems of Linear Equations

58m 34s

Intro
0:00
Introduction
0:12
Augmented Matrix
1:44
We Can Represent the Entire Linear System With an Augmented Matrix
1:50
Row Operations
3:22
Interchange the Locations of Two Rows
3:50
Multiply (or Divide) a Row by a Nonzero Number
3:58
Add (or Subtract) a Multiple of One Row to Another
4:12
Row Operations - Keep Notes!
5:50
Suggested Symbols
7:08
Gauss-Jordan Elimination - Idea
8:04
Gauss-Jordan Elimination - Idea, cont.
9:16
Reduced Row-Echelon Form
9:18
Gauss-Jordan Elimination - Method
11:36
Begin by Writing the System As An Augmented Matrix
11:38
Gauss-Jordan Elimination - Method, cont.
13:48
Cramer's Rule - 2 x 2 Matrices
17:08
Cramer's Rule - n x n Matrices
19:24
Solving with Inverse Matrices
21:10
Solving Inverse Matrices, cont.
25:28
The Mighty (Graphing) Calculator
26:38
Example 1
29:56
Example 2
33:56
Example 3
37:00
Example 3, cont.
45:04
Example 4
51:28
Section 11: Alternate Ways to Graph
Parametric Equations

53m 33s

Intro
0:00
Introduction
0:06
Definition
1:10
Plane Curve
1:24
The Key Idea
2:00
Graphing with Parametric Equations
2:52
Same Graph, Different Equations
5:04
How Is That Possible?
5:36
Same Graph, Different Equations, cont.
5:42
Here's Another to Consider
7:56
Same Plane Curve, But Still Different
8:10
A Metaphor for Parametric Equations
9:36
Think of Parametric Equations As a Way to Describe the Motion of An Object
9:38
Graph Shows Where It Went, But Not Speed
10:32
Eliminating Parameters
12:14
Rectangular Equation
12:16
Caution
13:52
Creating Parametric Equations
14:30
Interesting Graphs
16:38
Graphing Calculators, Yay!
19:18
Example 1
22:36
Example 2
28:26
Example 3
37:36
Example 4
41:00
Projectile Motion
44:26
Example 5
47:00
Polar Coordinates

48m 7s

Intro
0:00
Introduction
0:04
Polar Coordinates Give Us a Way To Describe the Location of a Point
0:26
Polar Equations and Functions
0:50
Plotting Points with Polar Coordinates
1:06
The Distance of the Point from the Origin
1:09
The Angle of the Point
1:33
Give Points as the Ordered Pair (r,θ)
2:03
Visualizing Plotting in Polar Coordinates
2:32
First Way We Can Plot
2:39
Second Way We Can Plot
2:50
First, We'll Look at Visualizing r, Then θ
3:09
Rotate the Length Counter-Clockwise by θ
3:38
Alternatively, We Can Visualize θ, Then r
4:06
'Polar Graph Paper'
6:17
Horizontal and Vertical Tick Marks Are Not Useful for Polar
6:42
Use Concentric Circles to Helps Up See Distance From the Pole
7:08
Can Use Arc Sectors to See Angles
7:57
Multiple Ways to Name a Point
9:17
Examples
9:30
For Any Angle θ, We Can Make an Equivalent Angle
10:44
Negative Values for r
11:58
If r Is Negative, We Go In The Direction Opposite the One That The Angle θ Points Out
12:22
Another Way to Name the Same Point: Add π to θ and Make r Negative
13:44
Converting Between Rectangular and Polar
14:37
Rectangular Way to Name
14:43
Polar Way to Name
14:52
The Rectangular System Must Have a Right Angle Because It's Based on a Rectangle
15:08
Connect Both Systems Through Basic Trigonometry
15:38
Equation to Convert From Polar to Rectangular Coordinate Systems
16:55
Equation to Convert From Rectangular to Polar Coordinate Systems
17:13
Converting to Rectangular is Easy
17:20
Converting to Polar is a Bit Trickier
17:21
Draw Pictures
18:55
Example 1
19:50
Example 2
25:17
Example 3
31:05
Example 4
35:56
Example 5
41:49
Polar Equations & Functions

38m 16s

Intro
0:00
Introduction
0:04
Equations and Functions
1:16
Independent Variable
1:21
Dependent Variable
1:30
Examples
1:46
Always Assume That θ Is In Radians
2:44
Graphing in Polar Coordinates
3:29
Graph is the Same Way We Graph 'Normal' Stuff
3:32
Example
3:52
Graphing in Polar - Example, Cont.
6:45
Tips for Graphing
9:23
Notice Patterns
10:19
Repetition
13:39
Graphing Equations of One Variable
14:39
Converting Coordinate Types
16:16
Use the Same Conversion Formulas From the Previous Lesson
16:23
Interesting Graphs
17:48
Example 1
18:03
Example 2
18:34
Graphing Calculators, Yay!
19:07
Plot Random Things, Alter Equations You Understand, Get a Sense for How Polar Stuff Works
19:11
Check Out the Appendix
19:26
Example 1
21:36
Example 2
28:13
Example 3
34:24
Example 4
35:52
Section 12: Complex Numbers and Polar Coordinates
Polar Form of Complex Numbers

40m 43s

Intro
0:00
Polar Coordinates
0:49
Rectangular Form
0:52
Polar Form
1:25
R and Theta
1:51
Polar Form Conversion
2:27
R and Theta
2:35
Optimal Values
4:05
Euler's Formula
4:25
Multiplying Two Complex Numbers in Polar Form
6:10
Multiply r's Together and Add Exponents
6:32
Example 1: Convert Rectangular to Polar Form
7:17
Example 2: Convert Polar to Rectangular Form
13:49
Example 3: Multiply Two Complex Numbers
17:28
Extra Example 1: Convert Between Rectangular and Polar Forms
-1
Extra Example 2: Simplify Expression to Polar Form
-2
DeMoivre's Theorem

57m 37s

Intro
0:00
Introduction to DeMoivre's Theorem
0:10
n nth Roots
3:06
DeMoivre's Theorem: Finding nth Roots
3:52
Relation to Unit Circle
6:29
One nth Root for Each Value of k
7:11
Example 1: Convert to Polar Form and Use DeMoivre's Theorem
8:24
Example 2: Find Complex Eighth Roots
15:27
Example 3: Find Complex Roots
27:49
Extra Example 1: Convert to Polar Form and Use DeMoivre's Theorem
-1
Extra Example 2: Find Complex Fourth Roots
-2
Section 13: Counting & Probability
Counting

31m 36s

Intro
0:00
Introduction
0:08
Combinatorics
0:56
Definition: Event
1:24
Example
1:50
Visualizing an Event
3:02
Branching line diagram
3:06
Addition Principle
3:40
Example
4:18
Multiplication Principle
5:42
Example
6:24
Pigeonhole Principle
8:06
Example
10:26
Draw Pictures
11:06
Example 1
12:02
Example 2
14:16
Example 3
17:34
Example 4
21:26
Example 5
25:14
Permutations & Combinations

44m 3s

Intro
0:00
Introduction
0:08
Permutation
0:42
Combination
1:10
Towards a Permutation Formula
2:38
How Many Ways Can We Arrange the Letters A, B, C, D, and E?
3:02
Towards a Permutation Formula, cont.
3:34
Factorial Notation
6:56
Symbol Is '!'
6:58
Examples
7:32
Permutation of n Objects
8:44
Permutation of r Objects out of n
9:04
What If We Have More Objects Than We Have Slots to Fit Them Into?
9:46
Permutation of r Objects Out of n, cont.
10:28
Distinguishable Permutations
14:46
What If Not All Of the Objects We're Permuting Are Distinguishable From Each Other?
14:48
Distinguishable Permutations, cont.
17:04
Combinations
19:04
Combinations, cont.
20:56
Example 1
23:10
Example 2
26:16
Example 3
28:28
Example 4
31:52
Example 5
33:58
Example 6
36:34
Probability

36m 58s

Intro
0:00
Introduction
0:06
Definition: Sample Space
1:18
Event = Something Happening
1:20
Sample Space
1:36
Probability of an Event
2:12
Let E Be An Event and S Be The Corresponding Sample Space
2:14
'Equally Likely' Is Important
3:52
Fair and Random
5:26
Interpreting Probability
6:34
How Can We Interpret This Value?
7:24
We Can Represent Probability As a Fraction, a Decimal, Or a Percentage
8:04
One of Multiple Events Occurring
9:52
Mutually Exclusive Events
10:38
What If The Events Are Not Mutually Exclusive?
12:20
Taking the Possibility of Overlap Into Account
13:24
An Event Not Occurring
17:14
Complement of E
17:22
Independent Events
19:36
Independent
19:48
Conditional Events
21:28
What Is The Events Are Not Independent Though?
21:30
Conditional Probability
22:16
Conditional Events, cont.
23:51
Example 1
25:27
Example 2
27:09
Example 3
28:57
Example 4
30:51
Example 5
34:15
Section 14: Conic Sections
Parabolas

41m 27s

Intro
0:00
What is a Parabola?
0:20
Definition of a Parabola
0:29
Focus
0:59
Directrix
1:15
Axis of Symmetry
3:08
Vertex
3:33
Minimum or Maximum
3:44
Standard Form
4:59
Horizontal Parabolas
5:08
Vertex Form
5:19
Upward or Downward
5:41
Example: Standard Form
6:06
Graphing Parabolas
8:31
Shifting
8:51
Example: Completing the Square
9:22
Symmetry and Translation
12:18
Example: Graph Parabola
12:40
Latus Rectum
17:13
Length
18:15
Example: Latus Rectum
18:35
Horizontal Parabolas
18:57
Not Functions
20:08
Example: Horizontal Parabola
21:21
Focus and Directrix
24:11
Horizontal
24:48
Example 1: Parabola Standard Form
25:12
Example 2: Graph Parabola
30:00
Example 3: Graph Parabola
33:13
Example 4: Parabola Equation
37:28
Circles

21m 3s

Intro
0:00
What are Circles?
0:08
Example: Equidistant
0:17
Radius
0:32
Equation of a Circle
0:44
Example: Standard Form
1:11
Graphing Circles
1:47
Example: Circle
1:56
Center Not at Origin
3:07
Example: Completing the Square
3:51
Example 1: Equation of Circle
6:44
Example 2: Center and Radius
11:51
Example 3: Radius
15:08
Example 4: Equation of Circle
16:57
Ellipses

46m 51s

Intro
0:00
What Are Ellipses?
0:11
Foci
0:23
Properties of Ellipses
1:43
Major Axis, Minor Axis
1:47
Center
1:54
Length of Major Axis and Minor Axis
3:21
Standard Form
5:33
Example: Standard Form of Ellipse
6:09
Vertical Major Axis
9:14
Example: Vertical Major Axis
9:46
Graphing Ellipses
12:51
Complete the Square and Symmetry
13:00
Example: Graphing Ellipse
13:16
Equation with Center at (h, k)
19:57
Horizontal and Vertical
20:14
Difference
20:27
Example: Center at (h, k)
20:55
Example 1: Equation of Ellipse
24:05
Example 2: Equation of Ellipse
27:57
Example 3: Equation of Ellipse
32:32
Example 4: Graph Ellipse
38:27
Hyperbolas

38m 15s

Intro
0:00
What are Hyperbolas?
0:12
Two Branches
0:18
Foci
0:38
Properties
2:00
Transverse Axis and Conjugate Axis
2:06
Vertices
2:46
Length of Transverse Axis
3:14
Distance Between Foci
3:31
Length of Conjugate Axis
3:38
Standard Form
5:45
Vertex Location
6:36
Known Points
6:52
Vertical Transverse Axis
7:26
Vertex Location
7:50
Asymptotes
8:36
Vertex Location
8:56
Rectangle
9:28
Diagonals
10:29
Graphing Hyperbolas
12:58
Example: Hyperbola
13:16
Equation with Center at (h, k)
16:32
Example: Center at (h, k)
17:21
Example 1: Equation of Hyperbola
19:20
Example 2: Equation of Hyperbola
22:48
Example 3: Graph Hyperbola
26:05
Example 4: Equation of Hyperbola
36:29
Conic Sections

18m 43s

Intro
0:00
Conic Sections
0:16
Double Cone Sections
0:24
Standard Form
1:27
General Form
1:37
Identify Conic Sections
2:16
B = 0
2:50
X and Y
3:22
Identify Conic Sections, Cont.
4:46
Parabola
5:17
Circle
5:51
Ellipse
6:31
Hyperbola
7:10
Example 1: Identify Conic Section
8:01
Example 2: Identify Conic Section
11:03
Example 3: Identify Conic Section
11:38
Example 4: Identify Conic Section
14:50
Section 15: Sequences, Series, & Induction
Introduction to Sequences

57m 45s

Intro
0:00
Introduction
0:06
Definition: Sequence
0:28
Infinite Sequence
2:08
Finite Sequence
2:22
Length
2:58
Formula for the nth Term
3:22
Defining a Sequence Recursively
5:54
Initial Term
7:58
Sequences and Patterns
10:40
First, Identify a Pattern
12:52
How to Get From One Term to the Next
17:38
Tips for Finding Patterns
19:52
More Tips for Finding Patterns
24:14
Even More Tips
26:50
Example 1
30:32
Example 2
34:54
Fibonacci Sequence
34:55
Example 3
38:40
Example 4
45:02
Example 5
49:26
Example 6
51:54
Introduction to Series

40m 27s

Intro
0:00
Introduction
0:06
Definition: Series
1:20
Why We Need Notation
2:48
Simga Notation (AKA Summation Notation)
4:44
Thing Being Summed
5:42
Index of Summation
6:21
Lower Limit of Summation
7:09
Upper Limit of Summation
7:23
Sigma Notation, Example
7:36
Sigma Notation for Infinite Series
9:08
How to Reindex
10:58
How to Reindex, Expanding
12:56
How to Reindex, Substitution
16:46
Properties of Sums
19:42
Example 1
23:46
Example 2
25:34
Example 3
27:12
Example 4
29:54
Example 5
32:06
Example 6
37:16
Arithmetic Sequences & Series

31m 36s

Intro
0:00
Introduction
0:05
Definition: Arithmetic Sequence
0:47
Common Difference
1:13
Two Examples
1:19
Form for the nth Term
2:14
Recursive Relation
2:33
Towards an Arithmetic Series Formula
5:12
Creating a General Formula
10:09
General Formula for Arithmetic Series
14:23
Example 1
15:46
Example 2
17:37
Example 3
22:21
Example 4
24:09
Example 5
27:14
Geometric Sequences & Series

39m 27s

Intro
0:00
Introduction
0:06
Definition
0:48
Form for the nth Term
2:42
Formula for Geometric Series
5:16
Infinite Geometric Series
11:48
Diverges
13:04
Converges
14:48
Formula for Infinite Geometric Series
16:32
Example 1
20:32
Example 2
22:02
Example 3
26:00
Example 4
30:48
Example 5
34:28
Mathematical Induction

49m 53s

Intro
0:00
Introduction
0:06
Belief Vs. Proof
1:22
A Metaphor for Induction
6:14
The Principle of Mathematical Induction
11:38
Base Case
13:24
Inductive Step
13:30
Inductive Hypothesis
13:52
A Remark on Statements
14:18
Using Mathematical Induction
16:58
Working Example
19:58
Finding Patterns
28:46
Example 1
30:17
Example 2
37:50
Example 3
42:38
The Binomial Theorem

1h 13m 13s

Intro
0:00
Introduction
0:06
We've Learned That a Binomial Is An Expression That Has Two Terms
0:07
Understanding Binomial Coefficients
1:20
Things We Notice
2:24
What Goes In the Blanks?
5:52
Each Blank is Called a Binomial Coefficient
6:18
The Binomial Theorem
6:38
Example
8:10
The Binomial Theorem, cont.
10:46
We Can Also Write This Expression Compactly Using Sigma Notation
12:06
Proof of the Binomial Theorem
13:22
Proving the Binomial Theorem Is Within Our Reach
13:24
Pascal's Triangle
15:12
Pascal's Triangle, cont.
16:12
Diagonal Addition of Terms
16:24
Zeroth Row
18:04
First Row
18:12
Why Do We Care About Pascal's Triangle?
18:50
Pascal's Triangle, Example
19:26
Example 1
21:26
Example 2
24:34
Example 3
28:34
Example 4
32:28
Example 5
37:12
Time for the Fireworks!
43:38
Proof of the Binomial Theorem
43:44
We'll Prove This By Induction
44:04
Proof (By Induction)
46:36
Proof, Base Case
47:00
Proof, Inductive Step - Notation Discussion
49:22
Induction Step
49:24
Proof, Inductive Step - Setting Up
52:26
Induction Hypothesis
52:34
What We What To Show
52:44
Proof, Inductive Step - Start
54:18
Proof, Inductive Step - Middle
55:38
Expand Sigma Notations
55:48
Proof, Inductive Step - Middle, cont.
58:40
Proof, Inductive Step - Checking In
1:01:08
Let's Check In With Our Original Goal
1:01:12
Want to Show
1:01:18
Lemma - A Mini Theorem
1:02:18
Proof, Inductive Step - Lemma
1:02:52
Proof of Lemma: Let's Investigate the Left Side
1:03:08
Proof, Inductive Step - Nearly There
1:07:54
Proof, Inductive Step - End!
1:09:18
Proof, Inductive Step - End!, cont.
1:11:01
Section 16: Preview of Calculus
Idea of a Limit

40m 22s

Intro
0:00
Introduction
0:05
Motivating Example
1:26
Fuzzy Notion of a Limit
3:38
Limit is the Vertical Location a Function is Headed Towards
3:44
Limit is What the Function Output is Going to Be
4:15
Limit Notation
4:33
Exploring Limits - 'Ordinary' Function
5:26
Test Out
5:27
Graphing, We See The Answer Is What We Would Expect
5:44
Exploring Limits - Piecewise Function
6:45
If We Modify the Function a Bit
6:49
Exploring Limits - A Visual Conception
10:08
Definition of a Limit
12:07
If f(x) Becomes Arbitrarily Close to Some Number L as x Approaches Some Number c, Then the Limit of f(x) As a Approaches c is L.
12:09
We Are Not Concerned with f(x) at x=c
12:49
We Are Considering x Approaching From All Directions, Not Just One Side
13:10
Limits Do Not Always Exist
15:47
Finding Limits
19:49
Graphs
19:52
Tables
21:48
Precise Methods
24:53
Example 1
26:06
Example 2
27:39
Example 3
30:51
Example 4
33:11
Example 5
37:07
Formal Definition of a Limit

57m 11s

Intro
0:00
Introduction
0:06
New Greek Letters
2:42
Delta
3:14
Epsilon
3:46
Sometimes Called the Epsilon-Delta Definition of a Limit
3:56
Formal Definition of a Limit
4:22
What does it MEAN!?!?
5:00
The Groundwork
5:38
Set Up the Limit
5:39
The Function is Defined Over Some Portion of the Reals
5:58
The Horizontal Location is the Value the Limit Will Approach
6:28
The Vertical Location L is Where the Limit Goes To
7:00
The Epsilon-Delta Part
7:26
The Hard Part is the Second Part of the Definition
7:30
Second Half of Definition
10:04
Restrictions on the Allowed x Values
10:28
The Epsilon-Delta Part, cont.
13:34
Sherlock Holmes and Dr. Watson
15:08
The Adventure of the Delta-Epsilon Limit
15:16
Setting
15:18
We Begin By Setting Up the Game As Follows
15:52
The Adventure of the Delta-Epsilon, cont.
17:24
This Game is About Limits
17:46
What If I Try Larger?
19:39
Technically, You Haven't Proven the Limit
20:53
Here is the Method
21:18
What We Should Concern Ourselves With
22:20
Investigate the Left Sides of the Expressions
25:24
We Can Create the Following Inequalities
28:08
Finally…
28:50
Nothing Like a Good Proof to Develop the Appetite
30:42
Example 1
31:02
Example 1, cont.
36:26
Example 2
41:46
Example 2, cont.
47:50
Finding Limits

32m 40s

Intro
0:00
Introduction
0:08
Method - 'Normal' Functions
2:04
The Easiest Limits to Find
2:06
It Does Not 'Break'
2:18
It Is Not Piecewise
2:26
Method - 'Normal' Functions, Example
3:38
Method - 'Normal' Functions, cont.
4:54
The Functions We're Used to Working With Go Where We Expect Them To Go
5:22
A Limit is About Figuring Out Where a Function is 'Headed'
5:42
Method - Canceling Factors
7:18
One Weird Thing That Often Happens is Dividing By 0
7:26
Method - Canceling Factors, cont.
8:16
Notice That The Two Functions Are Identical With the Exception of x=0
8:20
Method - Canceling Factors, cont.
10:00
Example
10:52
Method - Rationalization
12:04
Rationalizing a Portion of Some Fraction
12:05
Conjugate
12:26
Method - Rationalization, cont.
13:14
Example
13:50
Method - Piecewise
16:28
The Limits of Piecewise Functions
16:30
Example 1
17:42
Example 2
18:44
Example 3
20:20
Example 4
22:24
Example 5
24:24
Example 6
27:12
Continuity & One-Sided Limits

32m 43s

Intro
0:00
Introduction
0:06
Motivating Example
0:56
Continuity - Idea
2:14
Continuous Function
2:18
All Parts of Function Are Connected
2:28
Function's Graph Can Be Drawn Without Lifting Pencil
2:36
There Are No Breaks or Holes in Graph
2:56
Continuity - Idea, cont.
3:38
We Can Interpret the Break in the Continuity of f(x) as an Issue With the Function 'Jumping'
3:52
Continuity - Definition
5:16
A Break in Continuity is Caused By the Limit Not Matching Up With What the Function Does
5:18
Discontinuous
6:02
Discontinuity
6:10
Continuity and 'Normal' Functions
6:48
Return of the Motivating Example
8:14
One-Sided Limit
8:48
One-Sided Limit - Definition
9:16
Only Considers One Side
9:20
Be Careful to Keep Track of Which Symbol Goes With Which Side
10:06
One-Sided Limit - Example
10:50
There Does Not Necessarily Need to Be a Connection Between Left or Right Side Limits
11:16
Normal Limits and One-Sided Limits
12:08
Limits of Piecewise Functions
14:12
'Breakover' Points
14:22
We Find the Limit of a Piecewise Function By Checking If the Left and Right Side Limits Agree With Each Other
15:34
Example 1
16:40
Example 2
18:54
Example 3
22:00
Example 4
26:36
Limits at Infinity & Limits of Sequences

32m 49s

Intro
0:00
Introduction
0:06
Definition: Limit of a Function at Infinity
1:44
A Limit at Infinity Works Very Similarly to How a Normal Limit Works
2:38
Evaluating Limits at Infinity
4:08
Rational Functions
4:17
Examples
4:30
For a Rational Function, the Question Boils Down to Comparing the Long Term Growth Rates of the Numerator and Denominator
5:22
There are Three Possibilities
6:36
Evaluating Limits at Infinity, cont.
8:08
Does the Function Grow Without Bound? Will It 'Settle Down' Over Time?
10:06
Two Good Ways to Think About This
10:26
Limit of a Sequence
12:20
What Value Does the Sequence Tend to Do in the Long-Run?
12:41
The Limit of a Sequence is Very Similar to the Limit of a Function at Infinity
12:52
Numerical Evaluation
14:16
Numerically: Plug in Numbers and See What Comes Out
14:24
Example 1
16:42
Example 2
21:00
Example 3
22:08
Example 4
26:14
Example 5
28:10
Example 6
31:06
Instantaneous Slope & Tangents (Derivatives)

51m 13s

Intro
0:00
Introduction
0:08
The Derivative of a Function Gives Us a Way to Talk About 'How Fast' the Function If Changing
0:16
Instantaneous Slop
0:22
Instantaneous Rate of Change
0:28
Slope
1:24
The Vertical Change Divided by the Horizontal
1:40
Idea of Instantaneous Slope
2:10
What If We Wanted to Apply the Idea of Slope to a Non-Line?
2:14
Tangent to a Circle
3:52
What is the Tangent Line for a Circle?
4:42
Tangent to a Curve
5:20
Towards a Derivative - Average Slope
6:36
Towards a Derivative - Average Slope, cont.
8:20
An Approximation
11:24
Towards a Derivative - General Form
13:18
Towards a Derivative - General Form, cont.
16:46
An h Grows Smaller, Our Slope Approximation Becomes Better
18:44
Towards a Derivative - Limits!
20:04
Towards a Derivative - Limits!, cont.
22:08
We Want to Show the Slope at x=1
22:34
Towards a Derivative - Checking Our Slope
23:12
Definition of the Derivative
23:54
Derivative: A Way to Find the Instantaneous Slope of a Function at Any Point
23:58
Differentiation
24:54
Notation for the Derivative
25:58
The Derivative is a Very Important Idea In Calculus
26:04
The Important Idea
27:34
Why Did We Learn the Formal Definition to Find a Derivative?
28:18
Example 1
30:50
Example 2
36:06
Example 3
40:24
The Power Rule
44:16
Makes It Easier to Find the Derivative of a Function
44:24
Examples
45:04
n Is Any Constant Number
45:46
Example 4
46:26
Area Under a Curve (Integrals)

45m 26s

Intro
0:00
Introduction
0:06
Integral
0:12
Idea of Area Under a Curve
1:18
Approximation by Rectangles
2:12
The Easiest Way to Find Area is With a Rectangle
2:18
Various Methods for Choosing Rectangles
4:30
Rectangle Method - Left-Most Point
5:12
The Left-Most Point
5:16
Rectangle Method - Right-Most Point
5:58
The Right-Most Point
6:00
Rectangle Method - Mid-Point
6:42
Horizontal Mid-Point
6:48
Rectangle Method - Maximum (Upper Sum)
7:34
Maximum Height
7:40
Rectangle Method - Minimum
8:54
Minimum Height
9:02
Evaluating the Area Approximation
10:08
Split the Interval Into n Sub-Intervals
10:30
More Rectangles, Better Approximation
12:14
The More We Us , the Better Our Approximation Becomes
12:16
Our Approximation Becomes More Accurate as the Number of Rectangles n Goes Off to Infinity
12:44
Finding Area with a Limit
13:08
If This Limit Exists, It Is Called the Integral From a to b
14:08
The Process of Finding Integrals is Called Integration
14:22
The Big Reveal
14:40
The Integral is Based on the Antiderivative
14:46
The Big Reveal - Wait, Why?
16:28
The Rate of Change for the Area is Based on the Height of the Function
16:50
Height is the Derivative of Area, So Area is Based on the Antiderivative of Height
17:50
Example 1
19:06
Example 2
22:48
Example 3
29:06
Example 3, cont.
35:14
Example 4
40:14
Section 17: Appendix: Graphing Calculators
Buying a Graphing Calculator

10m 41s

Intro
0:00
Should You Buy?
0:06
Should I Get a Graphing Utility?
0:20
Free Graphing Utilities - Web Based
0:38
Personal Favorite: Desmos
0:58
Free Graphing Utilities - Offline Programs
1:18
GeoGebra
1:31
Microsoft Mathematics
1:50
Grapher
2:18
Other Graphing Utilities - Tablet/Phone
2:48
Should You Buy a Graphing Calculator?
3:22
The Only Real Downside
4:10
Deciding on Buying
4:20
If You Plan on Continuing in Math and/or Science
4:26
If Money is Not Particularly Tight for You
4:32
If You Don't Plan to Continue in Math and Science
5:02
If You Do Plan to Continue and Money Is Tight
5:28
Which to Buy
5:44
Which Graphing Calculator is Best?
5:46
Too Many Factors
5:54
Do Your Research
6:12
The Old Standby
7:10
TI-83 (Plus)
7:16
TI-84 (Plus)
7:18
Tips for Purchasing
9:17
Buy Online
9:19
Buy Used
9:35
Ask Around
10:09
Graphing Calculator Basics

10m 51s

Intro
0:00
Read the Manual
0:06
Skim It
0:20
Play Around and Experiment
0:34
Syntax
0:40
Definition of Syntax in English and Math
0:46
Pay Careful Attention to Your Syntax When Working With a Calculator
2:08
Make Sure You Use Parentheses to Indicate the Proper Order of Operations
2:16
Think About the Results
3:54
Settings
4:58
You'll Almost Never Need to Change the Settings on Your Calculator
5:00
Tell Calculator In Settings Whether the Angles Are In Radians or Degrees
5:26
Graphing Mode
6:32
Error Messages
7:10
Don't Panic
7:11
Internet Search
7:32
So Many Things
8:14
More Powerful Than You Realize
8:18
Other Things Your Graphing Calculator Can Do
8:24
Playing Around
9:16
Graphing Functions, Window Settings, & Table of Values

10m 38s

Intro
0:00
Graphing Functions
0:18
Graphing Calculator Expects the Variable to Be x
0:28
Syntax
0:58
The Syntax We Choose Will Affect How the Function Graphs
1:00
Use Parentheses
1:26
The Viewing Window
2:00
One of the Most Important Ideas When Graphing Is To Think About The Viewing Window
2:01
For Example
2:30
The Viewing Window, cont.
2:36
Window Settings
3:24
Manually Choose Window Settings
4:20
x Min
4:40
x Max
4:42
y Min
4:44
y Max
4:46
Changing the x Scale or y Scale
5:08
Window Settings, cont.
5:44
Table of Values
7:38
Allows You to Quickly Churn Out Values for Various Inputs
7:42
For example
7:44
Changing the Independent Variable From 'Automatic' to 'Ask'
8:50
Finding Points of Interest

9m 45s

Intro
0:00
Points of Interest
0:06
Interesting Points on the Graph
0:11
Roots/Zeros (Zero)
0:18
Relative Minimums (Min)
0:26
Relative Maximums (Max)
0:32
Intersections (Intersection)
0:38
Finding Points of Interest - Process
1:48
Graph the Function
1:49
Adjust Viewing Window
2:12
Choose Point of Interest Type
2:54
Identify Where Search Should Occur
3:04
Give a Guess
3:36
Get Result
4:06
Advanced Technique: Arbitrary Solving
5:10
Find Out What Input Value Causes a Certain Output
5:12
For Example
5:24
Advanced Technique: Calculus
7:18
Derivative
7:22
Integral
7:30
But How Do You Show Work?
8:20
Parametric & Polar Graphs

7m 8s

Intro
0:00
Change Graph Type
0:08
Located in General 'Settings'
0:16
Graphing in Parametric
1:06
Set Up Both Horizontal Function and Vertical Function
1:08
For Example
2:04
Graphing in Polar
4:00
For Example
4:28
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Lecture Comments (2)
 1 answerLast reply by: Professor Selhorst-JonesWed May 6, 2015 11:39 AMPost by enya zh on May 5, 2015For "Talking About Specific Entries", what if you had to talk about the 21th row and the 51th column? You have to write a2151 but would it be confused with row 215 column 1, or row 2 column 151?

### Matrices

Note: Many teachers and textbooks first introduce matrices as a way to solve systems of linear equations through augmented matrices, row operations, and Gauss-Jordan elimination. If you're looking for that, watch the first part of the lesson Using Matrices to Solve Systems of Linear Equations.
• A matrix (plural: matrices) is a rectangular array where each entry is a number.
• For a matrix with m rows and n columns, we say it has an order of m×n (This property is sometimes called size' or dimension'). We can also write order as Am ×n. If a matrix has equal numbers of rows and columns (m=n), we call it a square matrix.
• Matrices are usually denoted by capital letters.
• Two matrices A and B are equal if they have the same order and all their entries are equal.
• We can also talk about some specific entry in row i and column j (where i and j are standing in for numbers). As we use capitals to denote a matrix (A), we often use the corresponding lower case to denote its entries (a). We can talk about a specific entry by using the subscript ij on the letter (aij) to denote the ith row and jth column.
• With this idea in mind, we can see a matrix as a series of entries represented by various aij. This means instead of having to write the entire matrix out (like above) or just using a letter to denote the whole thing (A), we can refer to it by using a single representative entry to stand in for all entries:
 A = [ aij ].
Since i and j can change, aij is a placeholder for all of the entries in A.
• Given some matrix A and a scalar (real number) k, we can multiply the matrix by the number:
 kA = [ k ·aij ].
That is, every entry of A is multiplied by k. [Note that this is just like multiplying a vector by a scalar.]
• Given two matrices A and B that have the same order (m×n, the number of rows and columns), we can add the two matrices together:
 A + B = [aij + bij ].
That is, we add together entries that come from the same "location" in each matrix to create a new matrix. [Note that this is very similar to adding vectors component-wise.]
• If A is an m×n matrix and B is an n ×p matrix, we can multiply them together and create a new matrix AB that is order m×p, and which is defined as
 AB = [cij],
where cij = ai1b1j + ai2b2j + …+ ainbnj. That is, entry cij of AB (the entry in its ith row and jth column) is the sum of the products of corresponding entries from A's ith row & B's jth column. [The idea of matrix multiplication can be very confusing at first. Check out the video to see a lot of visual references to help explain what's going on here.]
• To multiply two matrices together, we have to first be sure that their orders are compatible. The numbers of columns in the first matrix must equal the number of rows in the second matrix.
• Multiplication in the real numbers is commutative, that is, x·y = y ·x: which side you multiply from does not affect the product. ( 5·7 = 7 ·5,   8(−3) = (−3)8 ). However, matrix multiplication is not commutative in general. That is, for most matrices A and B, AB ≠ BA.
• The zero matrix is a matrix that has 0 for all of its entries. A zero matrix can be made with any order. It is denoted by 0. [If you need to show its order: 0m×n.]
• The identity matrix is a square matrix that has 1 for all its entries on the main diagonal and 0 for all other entries. It can be any order, so long as it is square. It is denoted by I. (If you need to show it is order n ×n, you can denote by: In.) Notice that for any matrix A,  IA = A = A I. [ I effectively works the same as multiplying a real number by 1.]

### Matrices

A =

 8
 0
 −7
 −2

B =

 4
 −9
 −3
 9

Find A+B.
• To add two matrices together, we add together entries that come from the same "location" in each matrix to create a new matrix. [This is very similar to adding vectors together component-wise.]
• Just like we can represent numbers with letters and symbols, we can represent matrices with letters and symbols as well. Simply plug in what you know each matrix is from the problem.
A+B     =

 8
 0
 −7
 −2

+

 4
 −9
 −3
 9

• From there, add them together based on location:

 8
 0
 −7
 −2

+

 4
 −9
 −3
 9

=

 8+4
 0−9
 −7−3
 −2+9

=

 12
 −9
 −10
 7

[
 12
 −9
 −10
 7
]

X =

 −6
 9
 2
 −5

Find −4X.
• A "plain" number in front of a matrix is called a scalar. When we multiply a matrix by a scalar, the scalar multiplies every entry of the matrix. [This is very similar to multiplying a vector by a scalar.]
• Just like we can represent numbers with letters and symbols, we can represent matrices with letters and symbols as well. Simply plug in what you know each matrix is from the problem.
−4X     =     −4

 −6
 9
 2
 −5

• From there, multiply every entry of the matrix by the scalar:
−4

 −6
 9
 2
 −5

=

 (−4)(−6)
 (−4)9
 (−4)2
 (−4)(−5)

=

 24
 −36
 −8
 20

[
 24
 −36
 −8
 20
]

P =

 7
 1
 0
 −5

Q =

 −4
 6
 2
 −4

Find  5P−3Q.
• Multiplying a matrix by a scalar (a "normal" number) is as simple as having the scalar multiply every entry of the matrix. Adding or subtracting two matrices causes entries from the same location to combine through addition or subtraction, respectively.
• Begin by multiplying the scalars on to each matrix. (Notice that we can't combine them yet because the scalars are in the way. You couldn't do the addition in 7·4 + 2·8 before doing the multiplication for the same reason.) For this, we have two options: we could multiply (−3) on to Q, then add the resulting matrix, or we can multiply (3) on to Q, then subtract the resulting matrix. Either will work, but let's use (−3) just to help us see cancellation more easily.
5P − 3Q     =     5

 7
 1
 0
 −5

−3

 −4
 6
 2
 −4

=

 35
 5
 0
 −25

+

 12
 −18
 −6
 12

• Now we can combine the matrices through addition:

 35
 5
 0
 −25

+

 12
 −18
 −6
 12

=

 47
 −13
 −6
 −13

[
 47
 −13
 −6
 −13
]
Find the order of each of the below matrices:

 1
 3
 −5
 6
 4
 5.3
 19
 22
 −11
 −2
 7
 7
 18
 −4
 1.5
 4

,

 −7
 −17
 2
 16
 −3
 √ 47
 1
 4
 0
 π

,

 5

,

 0
 1
 2
 3
 6
 7
 8
 9

• The order of a matrix is a way of talking about its "size" or "dimensions". It tells us how many rows and columns the matrix has. Order is given in the format m ×n:
 Order of a matrix:     (# of rows) ×(# of columns)
• For the first matrix in the question (the left-most matrix), to find the order we just need to count how many rows and columns it has. It has a total of four rows and four columns, so its order is 4×4. [We would also call it a square matrix because the lengths of its "sides" are equal.]
• For the rest of the matrices, it's just a matter of counting up rows and columns. Second matrix from left: five rows and two columns, so 5×2. Third matrix from left: one row and one column, so 1 ×1. Fourth matrix from left: two rows and four columns, so 2 ×4.
Answers given in order of left to right: 4×4,     5 ×2,     1 ×1,     2 ×4

A =

 2
 5
 −1
 −3

B =

 3
 −4
 0
 6

Find AB.
• We will do this problem by matrix multiplication. We will need to multiply A and B together. Unlike matrix addition and scalar multiplication, matrix multiplication is very difficult to explain with words. If you are not already familiar with it, make sure to watch the video lesson and pay careful attention. It is much better explained with diagrams and watching steps happen, so make sure to check out the video. If you are already familiar with it, remember, we take the rows of the first matrix and multiply them against the columns of the second matrix. This creates the entry for the corresponding row and column.
• Following this idea, let's work out the first row, first column entry (the top left).
AB     =

 2
 5
 −1
 −3

 3
 −4
 0
 6

Thus, to find the first row, first column for the product of the two matrices, we take the first row of A and the first column of B:
First row of A:    2      5
First column of B:
 3
 0
Then we multiply them together (similar to how we did a dot product with vectors) as follows:
 2 ·3     +     5 ·0     =     6
And this gives us the first row, first column entry for the product

 2
 5
 −1
 −3

 3
 −4
 0
 6

=

 6
 ?
 ?
 ?

• Repeat this process for each of the entries in the prouct:

 2
 5
 −1
 −3

 3
 −4
 0
 6

=

 2 ·3 + 5 ·0
 2 ·(−4) + 5 ·6
 (−1) ·3 + (−3) ·0
 (−1) ·(−4) + (−3) ·6

Then simplify:

 2 ·3 + 5 ·0
 2 ·(−4) + 5 ·6
 (−1) ·3 + (−3) ·0
 (−1) ·(−4) + (−3) ·6

=

 6
 22
 −3
 −14

[
 6
 22
 −3
 −14
]

A =

 3
 1
 −5
 2

B =

 4
 −2
 6
 −7

Show that AB ≠ BA.
• Unlike "normal" multiplication, matrix multiplication is not commutative. That is, the side we multiply on has a huge effect on what the result is. To show that AB and BA are different for this problem, we need to start off by finding out what AB and BA are. [If you're unfamiliar with how to do matrix multiplication, make sure to watch the video lesson. It's very difficult to explain with written words, but watching it can really help you make sense of it.]
• Let's start off by computing AB:
AB     =

 3
 1
 −5
 2

 4
 −2
 6
 −7

Following the rules of matrix multiplication, we get

 3 ·4 + 1 ·6
 3 ·(−2) + 1 ·(−7)
 −5 ·4 + 2 ·6
 −5 ·(−2) + 2 ·(−7)

=

 18
 −13
 −8
 −4

• Next, we compute BA:
BA     =

 4
 −2
 6
 −7

 3
 1
 −5
 2

Following the rules of matrix multiplication, we get

 4 ·3 − 2 ·(−5)
 4 ·1 −2 ·2
 6 ·3 − 7 ·(−5)
 6 ·1 − 7 ·2

=

 22
 0
 53
 −8

• Finally, to show that AB ≠ BA, we just compare the two of them:
AB =

 18
 −13
 −8
 −4

BA =

 22
 0
 53
 −8

Clearly AB ≠ BA, so we have shown what the question asked.
AB = [
 18
 −13
 −8
 −4
]  and   BA = [
 22
 0
 53
 −8
], which shows that AB ≠ BA.

A=

 1
 3
 −5
 6
 4
 5
 19
 22
 −11
 −2
 7
 7
 18
 −4
 1
 4

,      B =

 5
 1
 8
 3

,      C =

 6
 −17
 2
 16
 −3
 47
 1
 4

If a matrix multiplication below is possible, give the order of the matrix it would result in. If it is not possible, say so.
 AC,       AB,        BC,        BAC
• For a matrix multiplication to be possible, the orders of the matrix must be compatible. That is, if some matrix X has an order of m×n and some matrix Y has an order of p ×q, the only way for XY to be possible is for n=p to be true. We can see it as the "inner" order numbers must be equal:
 Xm ×n  Yp ×q     ⇒     n = p
For two matrices to be able to multiply each other, the number of columns in the left matrix must match the number of rows in the right matrix. Assuming two matrices are compatible for multiplication, the resulting matrix will have an order based on the "outer" order numbers.
 Xm ×n  Yn ×q     =     (XY)m ×q
• To make this problem easier, begin by finding the order for each of the matrices: A has four rows and four columns, so order 4×4. B has one row and four columns, so order 1×4. C has four rows and two columns, so order 4 ×2.
• We can check to see if it is compatible and what type of matrix a given combination would produce by checking against orders:
 AC     ⇒     [4 ×4 ]  [4 ×2]     ⇒     [4 ×2]
The inner order numbers are equal, so it is compatible. The resulting matrix will have an order that uses the outer order numbers.
• Repeat this process for the other combinations:
 AB     ⇒     [4 ×4 ]  [1 ×4]     Impossible!
Because the inner order numbers are different, the matrices are incompatible for multiplication.
 BC     ⇒     [1 ×4 ]  [4 ×2]     ⇒     [1 ×2]
The inner order numbers are equal, so it is compatible. The resulting matrix will have an order that uses the outer order numbers.
• Things are a little bit different for the multiplication of BAC since it uses three matrices, but we can work it out based on starting with BA or AC and still get the same result:
 (BA)C     ⇒ ⎛⎝ [1 ×4 ]  [4 ×4 ] ⎞⎠ [4 ×2]     ⇒     [ 1 ×4 ]  [4 ×2]     ⇒     [1 ×2]

 B(AC)     ⇒     [1 ×4 ] ⎛⎝ [4 ×4 ]  [4 ×2 ] ⎞⎠ ⇒     [1 ×4 ]  [4 ×2 ]     ⇒     [1 ×2]
Thus BAC works fine for matrix multiplication, and we also see that the only important thing for matrix multiplication is that all of the inner orders pair up, and the resulting matrix comes from the outermost orders.
AC  ⇒  4×2,    AB not possible,     BC   ⇒  1 ×2,    BAC   ⇒  1 ×2

A =

 2
 1
 −3
 0
 −2
 1
 1
 −1
 4

B =

 0
 −3
 1
 2
 −2
 −1
 1
 5
 2

Find AB.
• For matrix multiplication, remember, we take the rows of the first matrix and multiply them against the columns of the second matrix. This creates the entry for the corresponding row and column. [If you're unfamiliar with how to do matrix multiplication, make sure to watch the video lesson. It's very difficult to explain with written words, but is shown with lots of diagrams and careful steps in the video to make it clear.]
• Put the matrices side-by-side so we can see how to match things up:
AB     =

 2
 1
 −3
 0
 −2
 1
 1
 −1
 4

 0
 −3
 1
 2
 −2
 −1
 1
 5
 2

By the rules of matrix multiplication, we get

 2 ·0 + 1 ·2 −3 ·1
 2 ·(−3) + 1 ·(−2) −3 ·5
 2 ·1 + 1 ·(−1) −3 ·2
 0 ·0 −2 ·2 + 1 ·1
 0 ·(−3) −2 ·(−2)+1 ·5
 0 ·1 −2 ·(−1) + 1 ·2
 1 ·0−1 ·2+4 ·1
 1 ·(−3) −1 ·(−2)+4 ·5
 1 ·1 −1 ·(−1) + 4 ·2

• From there, just simplify to find

 −1
 −23
 −5
 −3
 9
 4
 2
 19
 10

AB = [
 −1
 −23
 −5
 −3
 9
 4
 2
 19
 10
]
Simplify the below:
−3

 −2
 1
 0
 5
 −1
 0
 −3
 1

 5
 7
 1
 −3

+

 3
 −4
 −5
 −3

+

 −10
 1
 8
 3

• Begin by doing the scalar multiplication and the matrix addition:

 6
 −3
 0
 −15
 3
 0
 9
 −3

 −2
 4
 4
 −3

• At this point, we're left with two matrices multiplying each other

 6
 −3
 0
 −15
 3
 0
 9
 −3

 −2
 4
 4
 −3

Considering their orders of 2 ×4 and 4 ×1, we see that they are compatible for matrix multiplication and that the result will have an order of 2×1.
• Carry out the matrix multiplication:

 6 ·(−2) −3 ·4 + 0 ·4 −15 ·(−3)
 3 ·(−2) + 0 ·4 + 9 ·4 −3 ·(−3)

=

 21
 39

[
 21
 39
]

A =

 4
 2
 −1
 3

B =

 2
 −8
 7
 5

In the equation  3X = 2A−B, solve for X.
• We can work with a matrix equation very similarly to how we're used to working with an equation. As long as we do the exact same operation to both sides, the equation holds up, just like normal algebra. Our goal is to solve for X. Start off by writing it out with A and B plugged in:
3X = 2A−B     ⇒     3X = 2

 4
 2
 −1
 3

−

 2
 −8
 7
 5

• Simplify the right side:
3X =

 8
 4
 −2
 6

−

 2
 −8
 7
 5

=

 6
 12
 −9
 1

• We can now solve for X:
3X =

 6
 12
 −9
 1

⇒     1

3
·3X = 1

3
·

 6
 12
 −9
 1

⇒    X =

 2
 4
 −3
 1 3

X = [
 2
 4
 −3
 [1/3]
]

*These practice questions are only helpful when you work on them offline on a piece of paper and then use the solution steps function to check your answer.

Answer

### Matrices

Lecture Slides are screen-captured images of important points in the lecture. Students can download and print out these lecture slide images to do practice problems as well as take notes while watching the lecture.

• Intro 0:00
• Introduction 0:08
• Definition of a Matrix 3:02
• Size or Dimension
• Square Matrix
• Denoted by Capital Letters
• When are Two Matrices Equal?
• Examples of Matrices 6:44
• Rows x Columns
• Talking About Specific Entries 7:48
• We Use Capitals to Denote a Matrix and Lower Case to Denotes Its Entries
• Using Entries to Talk About Matrices 10:08
• Scalar Multiplication 11:26
• Scalar = Real Number
• Example
• Matrix Addition 13:08
• Example
• Matrix Multiplication 15:00
• Example
• Matrix Multiplication, cont.
• Matrix Multiplication and Order (Size) 25:26
• Make Sure Their Orders are Compatible
• Matrix Multiplication is NOT Commutative 28:20
• Example
• Special Matrices - Zero Matrix (0) 32:48
• Zero Matrix Has 0 for All of its Entries
• Special Matrices - Identity Matrix (I) 34:14
• Identity Matrix is a Square Matrix That Has 1 for All Its Entries on the Main Diagonal and 0 for All Other Entries
• Example 1 36:16
• Example 2 40:00
• Example 3 44:54
• Example 4 50:08

### Transcription: Matrices

Hi--welcome back to Educator.com.0000

Today, we are going to talk about matrices.0002

In some way, matrices are a natural extension of vectors.0004

Consider that we can express a vector as a horizontal array of numbers, where an array is just a bunch of different spaces to put numbers.0007

So, each component from a vector would be an entry in that array of numbers.0014

So, if we had some vector, (5,47,-8), we could also put that as 5, and then a little bit of space, and then 47, and then a little bit of space, and then -8.0018

We have this array that is three different locations for numbers to go, this rectangular array.0026

A matrix takes this idea and expands on it.0031

The vector was just a single line--it was just a single row going on.0034

Instead of just having columns of numbers (we had that single row with three different columns),0040

we can take that, and we can have rows and columns.0044

This allows us to show lots of information in a single array.0050

Just like a vector allowed us to show more information than a single number, a matrix will allow us to show even more information than a single vector.0053

So, it is a way to compact lots of information in this single, really useful thing.0060

And we will end up seeing how they are useful later on.0064

Matrices have a huge number of uses, both in math and other fields--they are really, really useful things0067

for science, computer science, engineering, business, economics...so many things.0073

But it is going to take a couple of lessons before we can see how useful they are,0079

because we have to just get the basics of how they work learned before we can really see an application.0082

But in two lessons, we will see how ridiculously easy they make it to solve linear systems.0087

So, once we have matrices learned, and have a good understanding of them, we will be able to solve linear systems easily, which is really cool.0093

Also, I want to mention: this lesson right here is going to be on how matrices work, what a matrix is, and how they interact in various different ways.0101

But many teachers and textbooks don't start with matrices as how a matrix works;0111

they start with it as specifically using it to solve linear equations0116

through augmented matrices, row operations, and Gauss-Jordan elimination.0120

If that is what you are looking for, you have a math class and you are trying to get more understanding of those things,0125

you are going to want to take a look at the lesson Using Matrices to Solve Systems of Linear Equations.0130

The first half of that lesson will go over augmented matrices, row operations, Gauss-Jordan elimination...0135

And there will be some examples about how that stuff works there.0141

So, if that is what you are looking for right now, you might want to go check that out, as opposed to this lesson.0143

However, that said, you are going to end up coming back to everything that is in this lesson.0147

It is just a question of if you introduce the idea of matrices through that stuff first, and then go on to talk about how they work;0151

or if you talk about how matrices work, and then you get to that stuff later.0157

I prefer talking about matrices first, and then getting to the applications; but it depends, from teacher to teacher and textbook to textbook.0160

So, in your case, you might be interested in watching that lesson first.0166

But you are going to end up coming back to this lesson and watching it anyway.0168

And some of the stuff in that lesson will make more sense if you watch this one first.0171

So, you might even find it worthwhile to watch this lesson before you get around to watching that, if you have time.0174

All right, let's get into this: a single object is a matrix, but if we are talking about multiple of them, in the plural, it is matrices.0178

It is a rectangular array where each entry is a number.0189

So, an array is just...imagine a bunch of boxes stacked together to make a rectangle of boxes.0191

And inside of each box, you can put in numbers; so you can put a number here, a number here, a number here, a number here, a number here...0198

And we call each of those places where you could put a number an entry.0204

We have some a = some number here, some number here, going all the way down to some number here,0208

some number here, some number here, going all the way down to some number here;0214

and the same thing going right as well...and then there are a bunch of numbers in the middle.0217

So, it is just a rectangular array--a bunch of places to put numbers in this nice rectangular thing.0222

It is like looking at a piece of grid paper and boxing off some part of it, and then writing a number inside of each of the grids.0228

All right, for a matrix with m rows and n columns (notice that it has m rows and n columns), we say it has an order of m by n.0237

We can put these two things together to talk about the order of the matrix.0255

This property is also sometimes called size or dimension; those are sometimes used as synonyms for order.0259

For the most part, we will just use "order" in this course, but I might say "size" occasionally.0264

We can also write order as Am x n: we write a little part underneath it, m x n.0268

So, if we mainly want to talk about some specific matrix, A for example, we can talk about A.0274

But if we want to mention its order as we are talking about A, we can write its order down to the side as a subscript, m x n.0279

If a matrix has equal numbers of rows and columns, if they are the same number of rows and columns,0286

m = n, we call it a square matrix, because we have a square object.0291

Matrices are usually denoted by capital letters, like A, but you might see other ones, as well.0296

Two matrices, A and B, are equal if they have the same order (they are the same size), and all of their entries are equal.0303

They have the same size, and then, if we go to any given one of the locations over here, it is the same as the same location over here.0310

We go to some location here; it is the same as the location over here.0317

You choose one location here; it is the same thing as this location here.0320

So, they have to look exactly the same for them to be equal to each other.0323

I also really want to drive home this fact that it is an m x n matrix with rows by columns.0327

It is always rows by columns; I found this a little bit confusing at first, but I would recommend:0338

the way to think about this, as a row, is something that goes left-right; a column is something that goes up-down.0344

So, whenever we are talking about stuff in math, we normally talk left-right, then up-down,0353

(x,y) when we are talking about rectangular coordinates.0358

So, when we are on the plane, we talk about the horizontal stuff, and then the vertical stuff,0361

which is why we talk about the rows, which horizontal thing we are talking about,0367

and then the columns, which vertical thing we are talking about.0371

It might get a little bit confusing as you work through it.0374

But just always remember: it is rows then columns; this order of rows, then columns, ends up being very important0376

for a lot of stuff--the way we talk about specific entries.0383

So, it is just really important to remember this "rows by columns."0385

The best mnemonic I can offer you is thinking in terms of the fact that rows are left-right; columns are up-down;0389

and we go left-right, then up-down, so it is rows, then columns, rows by columns.0396

But it is something that you just have to remember.0401

All right, with this idea in mind, that it is rows by columns, let's look at a couple of examples.0404

If we have a 3x3 matrix, then that means we have 3 rows, and we have 3 columns.0413

If we have a 2x3 matrix, then we have 2 rows, and we have 3 columns.0421

If we have a 5x1 matrix, then we have 5 rows, and we have 1 column--the same thing for all of them.0428

Also, I want to point out some of the numbers here.0436

We can have just whole numbers, like 17; but it is also perfectly fine to have decimal numbers, like 4.2.0438

We can have negative numbers, like -19; we can also have irrational numbers, like √2 or π.0445

We can have fractions: -5/7, 1/2; anything that is a real number at all can be one of the entries in a matrix.0452

Any number at all can be something inside of a matrix.0459

Talking about specific entries: we can also talk about some specific entry that is in row i and column j in our matrix.0464

And so, i and j are just standing in for numbers; we will swap them out for numbers later, when we need to.0471

In A that is a 3x3, this matrix right here, the entry in row 2, column 3 is 8.0476

So, we go to row 2: 1, 2; so we are on this one here; and column 3: 1, 2, the third column; so we are on this column here.0486

They end up intersecting right here, and so, we have row 2, column 3, is 8.0498

All right, we can expand on this idea: we use capitals to denote a matrix, like A.0510

So, we can often use corresponding lowercase letters to denote the entries inside of it--0517

so, A to denote the entire matrix, and a if we want to talk about some specific entry inside of it.0522

We can talk about a specific entry by using a subscript, ij, where a subscript--here is our number, and then ij,0528

or any subscript, is just numbers that are down and to the right of the number; that is where we have our subscripts.0535

So, we have ij on it; so we can combine those two, and we have ai,j, subscript ij.0543

And that will denote the ith row and jth column.0549

i came first, so that is talking about the rows; j came second, so that is talking about the columns.0552

So, ai,j is the ith row, jth column.0558

So, that means we could talk about a1,1: that would be first row, first column, so we would get 17.0563

We could talk about a2,1; that would be second row, first column; so that would be 0.0570

Second row, second column is also 0; we could have a2,3, second row, third column; so that would be 8.0576

That is exactly what we figured out at the beginning: row 2, column 3, is a2,3.0585

Or we could have a3,2, third row, second column, which gets us 3.0590

So, this gives us another way to talk about where a number is.0597

We can talk about it in terms of this entry, and a subscript to say which of the entries it is.0601

With this idea in mind, we have another way to talk about a matrix.0607

As opposed to a matrix being the entire matrix, or a matrix just being this capital letter that represents it,0610

we can see it as a series of entries represented by this ai,j.0615

There is a first row first column, first row second column, first row third; then second row first column, second row second column, third row second column, etc.0620

It is just a bunch of entries making up the whole thing.0628

With this idea in mind, instead of having to write the entire matrix like this...we don't have to do the entire matrix.0632

We don't also have to just use a single letter to denote the whole thing, like just A.0642

We can instead refer to it by using a single representative entry to stand in for all entries, ai,j.0647

So, it is like saying, "Here is some ai,j that is talking about all of the different things at once."0653

So, we can see what happens to this one that is representing all of them at once.0659

Notice: since i and j can change, ai,j is a placeholder for all of the entries in A.0664

It is not just one thing; it is all of them at once.0670

In a way, it is representing all of them at once by letting us see how something happens to one of them in there.0674

So, i,j is ith row, jth column; so we have another way to talk about a matrix.0680

All right, at this point we are ready to actually talk about how we can do some basic arithmetic with our matrices.0686

Given some matrix A and a scalar (that is to say, just a real number k), we can multiply the matrix by that number.0692

k times the matrix A becomes k times ai,j, that is, each of the entries of our matrix A gets multiplied by k.0699

So, every entry of A is multiplied by k.0708

Notice that this is just like multiplying a vector by a scalar.0711

If we have some vector, and we multiply it by a scalar, then that scalar multiplies on each of the components of the vector.0714

It is scaling the vector; it is multiplying each of the components.0721

So, if we have a scalar, and we multiply a matrix, that scalar multiplies each of the entries,0723

because a matrix doesn't have components; it has entries, because we have to talk about every row.0728

A vector is just a single row, but a matrix is many, so we talk about multiplying all of the entries.0734

So, other than that distinction between entries or components, it is very much the same thing.0739

A scalar on a vector multiplies each of its numbers.0744

A scalar on a matrix multiplies each of its numbers--it is basically the same thing.0747

So, let's look at a quick example: if we have 3 multiplying on the matrix 1, -4, 10; -19, -7, 20;0752

then we have that 3 multiplies on the first row, first column;0759

and that is going to get 3 times 1, which gets us 3; so the same location is now multiplied by 3.0763

3 times -4 gets us -12; the same location is just multiplied by 3.0769

3 times 10 gets us 30; 3 times -19 is -57; 3 times -17 is -21; 3 times 20 is 60; great.0773

Matrix addition: given two matrices, A and B, that have the same order (they have to have the same order;0783

otherwise it won't work--we will see why that is in just a second), we can add the two matrices together.0789

So, A + B: every ith row, jth column of the resultant matrix will end up being ai,j + bi,j.0794

That is to say, we are adding together entries that come from the same location.0803

If this one was from this place over here, and this one was from this place over here, these two different numbers,0809

we add them together, and that comes out to be that new place in our new matrix that we are creating.0813

Note that this is very similar to adding vectors component-wise; it is very much the same thing as when we added vectors.0818

If you add two vectors, you just take the first components, and you put them together;0824

the second components--you put them together; the third components--you add them together, until you get through the entire vector.0828

If we are doing it with a matrix, it is the same thing, except, instead of components, we now have to do it to each of the entries.0833

So, first row, first column entries: you add them together; first row, second column entries--you add them together, until you get done with that row.0838

Then second row, first column entries--you add them together; second row, second column entries--you add them together;0845

second row, third column...etc., until you have made it through all of the rows and all of the columns.0850

You take a given location; you put the things together from that location; that gives you the value for the same location in the new matrix.0854

Let's look at an example: if we have the matrix 4, 8, -3, 7, and 1, 3, 3, 0, we take first row, first column in both of them,0862

so 4 + 1; and that puts out 5 in the first row, first column of our new matrix.0870

The same thing for first row, second column: 8 and 3 are in them, respectively: 8 + 3 becomes 11; first row, second column0875

is the same location as what it just came from, in our new matrix.0884

The same thing over here: -3 + 3 becomes 0; and finally, 7 + 0 becomes positive 7; great.0887

So, we are keeping the location and adding them together, and that is what we get in our new matrix.0895

Matrix multiplication: now, this one is going to be very different.0900

The previous two made sense; they were a lot like what we were used to doing with vectors.0905

You multiply everything with a scalar; you add based on location with addition.0909

Matrix multiplication--this one is going to twist your brain a little bit.0914

So, it is confusing at first; but the applications in a couple of lessons will hopefully make us see0917

why we end up doing this kind of confusing thing, because there ends up being some purpose to this stuff.0923

But for now, we are not going to really have a very good understanding of why that has to be the case.0929

So, we just want to be careful and follow the rules precisely and pay close attention when you multiply matrices.0933

It is really, really easy to make mistakes with multiplying matrices, especially the first couple of times you are doing it.0938

So, you really have to be very careful and pay attention.0944

So, just follow these rules carefully; it is going to be confusing at first, but don't worry.0946

As we work through a bunch of examples, it will make a lot more sense.0950

The formal definition, the first thing that we are going to see, is probably the most confusing thing of all.0953

But as we see it in action, it will start to make a lot more sense.0957

So, just work through it; you will end up understanding this by the time we get to the examples--no problem.0959

All right, if we have some matrix A, and it is an m x n matrix, and B is an n x p matrix, we can multiply them together.0964

Notice that the m here and the m here match up: there are m rows and n columns in our first matrix,0971

and n rows, p columns in our second matrix; so the number of columns in the first matrix0979

matches up with the number of rows in the second matrix; that is an important idea--it will come up later on.0984

We can multiply them together, and we create a new matrix, AB.0989

That is going to end up being m x p, the things that didn't match up.0994

Or they could match up; but they don't have to match up.0998

And we define AB as: AB, the ith row, jth column of AB becomes ci,j,1000

where ci,j is equal to ai,1b1,j + ai,2b2,j,1007

up until we get to ai,nbn,j.1013

What does that mean? Let's look at that a little bit.1016

ai,1 is the ith row of A, first entry.1018

The b1,j is the first entry of the jth column, because it is the first row, but in our jth column.1029

So, it is the first entry; so it is the first entry, ith row, A; first entry, jth column, B.1035

ai,2 is second entry, ith row of A; and b2,j is second entry, jth column of B.1042

So, we multiply those together; we add them together with the other ones.1050

We keep doing this down the line, where it is the nth entry of ith row of A,1052

and the nth entry of the jth column of B.1057

Notice that the nth entry, in both of those cases, ends up being the last entry of that matrix.1061

If A is an m x n matrix, then for our A right here, i,n, well, the ith row has to stop at the nth entry,1065

because it only has n many columns to work its way through.1073

The same thing with bn,j: the nth entry in the jth column has to stop there,1076

because it has only n many rows to work through, to have things there.1081

So, that ends up stopping; and they stop at the same place, which is useful.1084

All right, so that is the entry ci,j of AB, the product of the two.1088

The entry in its ith row and its jth column is the sum of the products1092

of corresponding entries from A's ith row and B's jth column.1099

So, we are looking at the ith row of our first one--our first matrix, A, its ith row--1103

times the jth column of B, our second matrix.1109

We are multiplying them together, based on first entries, second entries, third entries, fourth entries...1115

We multiply them together, and then we add them all up together.1121

And that ends up giving us the value for the resultant product matrix in its ith row and jth column.1124

I know that it is confusing right now; it will make a lot more sense as soon as we start working on examples.1130

So, we can see this visually as taking the ith row (this is the ith row of A),1135

and then here--this would end up being the jth column of B.1144

Our first matrix's ith row, times the second matrix's jth column: we multiply them together,1154

where ai,1 is multiplied times b1,j, plus ai,2 times b2,j...1162

The first entry here is multiplied times the first entry here; the second entry here, times the second entry here;1167

the third entry here, times the third entry here.1173

We multiply them all together like that; then we sum everything up.1178

And that ends up producing ci,j, which is the ith row and jth column.1182

All right, that is what we end up getting here.1193

All right, we are ready for an example.1195

Let's look at how we would find the entry in the first row and third column of the product from the matrices below.1198

If we are looking for the first row, then that is going to be the first row of our first matrix, so 2, -1.1204

Then, the third column: columns are going to come from the second matrix: so third column...1, 2, 3..the third column here: 5, 0.1213

So, the first entries are 2 times 5; so that is 2 times 5, plus -1 times 0; that gets us 10, so 10 is what goes here in the first row and third column.1223

That is what we end up getting; we end up getting this number 10.1246

We are taking that first row, the third column; we are multiplying together in this strange way.1250

We are adding up, and we are plugging that in for the entry in the matrix that we are creating.1256

Now, notice that this bears some resemblance to dot products.1264

We can think of this ith row as being a vector, because it just has a bunch of pieces to it.1267

It has a bunch of components to it, since it is just one dimension in one way.1272

It is just a vector in one way: 2 and then -1.1276

And then, we have this jth column over here; we can think of this as also being a column vector.1280

We have this vector here and this vector here; we are taking the dot product of them: 2, -1 dotted with 5,0.1285

2 times 5 is 10; -1 times 0 is 0; so we get a total of 10.1292

So, we can think of it as being the ith row, dotted with the jth column.1296

If you think that is confusing--if you never really had a very good understanding of how dot products work in vectors--that is perfectly fine.1301

Don't worry about that; just think of it in terms of multiplying and multiplying like this.1307

But if the dot product stuff made a lot of sense to you in vectors,1311

you can think of it as turning this row into a vector briefly, turning this column into a vector briefly,1314

taking the dot product, and then moving on and doing the same thing with new vectors in a sort-of vector set.1318

It is not exactly like vectors, because we are working inside of a matrix.1324

But it is working very much under that same idea of multiplying based on location of entry, and then adding it all together.1327

All right, let's work this whole thing out: we will use red to talk about everything that this first row is going to.1335

What is the size of this going to come out to?1340

First, let's figure that out, so we can draw in bars for where we are going to multiply.1342

This is a 1, 2, 3...so it is a 3 x 2 matrix, because it has 2 columns.1347

And this has 2 rows and 3 columns, so it is a 2 x 3 matrix; so the 2 and the 2 match up here,1354

so it is going to end up coming out over here; our size is a 3 x 3 matrix.1362

And that also makes sense, because in our first matrix, we have three rows; and in our second matrix, we have three columns.1366

So, each of the things that will come out in our product is a way of putting a row and a column together.1372

Three rows; three columns; they end up stacking into a 3 x 3 product matrix.1377

All right, with that in mind, we know that what is going to have to come out of this is a 3 x 3 matrix.1383

So, I will leave enough room, approximately, to put in a 3 x 3 matrix inside of there.1388

The first one: the first row, first column, will give us the location that is the first row, first column in our product matrix.1394

2 times 2 and -1 times -3, then added together: 2 times 2 is 4; -1 times -3 is positive 3; so 4 + 3 is 7.1402

2, -1 on 1, 3 (first row on second column): 2 times 1 is 2; -1 times 3 is -3; add those together, and you get -1.1414

2, -1 on 5, 0: 2 times 5 is 10; -1 times 0 is 0; so we get 10.1424

So, there is our first row, after we have worked through all three columns.1430

The next one; let's use a new color here: 3, 4 on 2, -3; 3 times 2 gets us 6; 4 times -3 gets us -12; so it comes out to -6.1435

3, 4 on 1, 3; 3 times 1 is 3; 4 times 3 is 12; so that gets us 15 when we add them together.1447

3, 4 on 5, 0; 3 times 5 is 15; 4 times 0 is still 0; so that totals to 15.1454

The last one, the final color: 0, 5 on 2, -3; 0 times 2 is 0; 5 times -3 is -15; 0 times 1 is 0; 5 times 3 is +15;1460

and 0 times 5 is 0; 5 times 0 is 0; 0; and that is our final result.1474

So, we are working through, taking a row in our first matrix, then multiplying it against a column in our second matrix.1481

And we are doing location of entry: first entries, second entries, third entries, fourth entries, as many as we have entries.1491

We multiply the location of entries (first entries together, second entries together, third entries together...)--1497

multiply based on that, and then sum up the whole thing; and that is what gets us what comes out1501

as our product for that row number and that column number.1506

It makes a lot more sense after you just end up working with it, after you end up getting some practice in.1510

As soon as you start working on examples like this yourself, as soon as you do some practice homework, it will make a lot more sense.1515

But we will also get the chance to work on another example a little bit later.1520

All right, matrix multiplication and order: to multiply two matrices together, we have to first be sure that their orders are compatible.1524

We have talked about this a little bit so far.1530

The numbers of columns in the first matrix must equal the number of rows in the second matrix.1532

The number of columns in the first matrix must be the same as the number of rows in the second matrix.1538

And then, what comes out of it is this m x p; so ABm x p.1544

So, we have n columns in our first matrix, times n rows in our second matrix.1549

Why is this the case? We can just believe this rule, but let's also get a sense for why it is the case.1558

Well, consider this: if I have, say, a 3 x 2 matrix (let's use red, so we can see how it matches here),1563

and then we have something here, something here, something here, something here, something here, something here;1582

notice that if you look at the length of any row, the length of any row is 2.1589

The length of a row is based on how many columns you have, because each column is an entry.1594

If we look at a row, then it is going to span all of those columns; so it is going to be a question1599

of how many times it has something to go inside of the row.1603

Well, that is going to be a question of how many columns are going through that row.1606

So, the number of entries in a row is going to be based on the number of columns.1610

Similarly, if we have a 2 x 3 matrix, then it is going to be 2 rows, 3 columns.1615

If we grab some column, how many entries are going to be in the column?1624

Well, it is how many rows it goes deep.1628

So, the number of rows is going to tell us how many entries are in a given column.1631

Now, the way matrix multiplication works is: it is this thing, the row, times this thing, the column.1637

It is the row times the column; well, this whole thing has to be first entries against first entries,1644

second entries against second entries, third entries against third entries...1650

So, we have to have the number of entries match up.1653

If we have a different length in the row than the column--they are different lengths, row versus column--we are not going to have them match up.1656

This thing doesn't really make sense; so we are required...the idea of this is for the length here to match up to the length here.1664

And that is why we have this requirement: because the length of a row is based on how many columns it has.1670

The length of a column is based on how many rows it has.1675

So, that is why we have to have these matching here.1678

Otherwise, it won't make sense for the way we have this thing defined, because we will have something longer than the other thing;1682

and what do you multiply by then?--because you don't have the same number of entries; it doesn't make any sense.1687

When you take dot products with vectors, they have to have the same number of components for you to be able to take a dot product.1692

It is sort of the same thing going on here.1696

Matrix multiplication is not commutative; this is absolutely mind-blowing,1699

because it is not something that we have seen anywhere else in math at this point, I am pretty sure.1704

So, at this point, we probably want to know what it means to be commutative,1709

before we try to understand matrices not being commutative.1713

Let's look at that: commutative means that x times y is the same thing as y times x--1716

that this operation from the left is the same thing as the operation from the right.1722

x on the left of y is the same thing as x on the right of y; x times y equals y times x.1726

It doesn't matter which direction that x multiplies from; you get the same thing out of it, at least in the real numbers.1733

5 times 7 is the same thing as 7 times 5; 8 times -3 is the exact same thing as -3 times 8.1738

So, that is something we are pretty used to that makes a lot of sense to us.1746

It doesn't matter which direction you multiply from; it comes out to be the same thing.1749

So, we have never had to worry about it.1751

Well, it is time to start worrying about it: matrices are not commutative, in general.1753

That is, for most matrices A and B, AB is not equal to BA.1758

It is totally different if A multiplies on the left side, or if A multiplies on the right side; you will get totally different things.1764

Now, there are some cases when AB will be equal to BA; it is not an absolute, hard-and-fast rule that AB can never equal BA.1770

It is just like 99% of the time that AB will not be equal to BA.1778

Given two random matrices, chances are that they are not going to end up being the same, depending on the order of multiplication.1782

So, you have to pay attention to who is multiplying from which side.1790

You will have totally different things, depending on changing the order of multiplication, usually.1793

There are some cases where it won't be, but for the most part, they are totally different things.1797

So, you can't rely on having x times y equal to y times x, because all of a sudden, it is not equal to the same thing.1801

You are going to have to pay attention to the order that things are multiplying.1807

Let's look at an example to really make this clear.1811

If we have this first matrix, 4, 2, -3, 1, and 3, 0, -5, 2; then we know we are going to get a 2 x 2 matrix out of this, because they are both 2 x 2.1813

So, 4, 2, 3, -5....4 times 3 gets us 12; 2 times -5 gets us -10; so that comes out to 2.1822

4, 2 on 0, 2: 4 times 0 is 0; 2 times 2 is 4; -3 on 1...let's use a new color here; -3, 1 on 3, -5; -3 times 3 gets us -9; 1 times -5 gets us -5; so -14 total.1829

-3, 1 on 0, 2; -3 times 0 is 0; 1 times 2 is 2; OK, so that is what that first matrix came out to be.1846

What about this one here, where we flipped the order of multiplying them?1854

We have 3, 0 on 4, -3 now; once again, it is going to come out as a 2 x 2 matrix: 3, 0 on 4, -3:1857

3 times 4 is 12; 0 times -3 is 0; so we have 12.1865

At this point, we already see that we are not the same; on the first one we did, that first multiplication,1869

our first row, first column was 2; in the second one, our first row, first column, was 12.1875

2 versus 12 is totally different; we know that these matrices cannot be the same anymore,1881

because one of their entries is different, and that is enough to say that they are not equal.1886

However, let's get a sense for just how different they are; let's look at the rest of this thing.1890

3, 0...the first row, on the second column now...on 2, 1: 3 times 2 gets us 6; 0 times 1 gets us 0; so 6.1894

-5, 2 on 4, -3; -5 times 4 is -20; 2 times -3 is -6; so -26.1902

-5, 2 on 2, 1: -10 + 2 gets us -8.1911

So, notice: these things are totally and utterly different.1916

2, 4, -14, 2 is completely different than 12, 6, -26, -8.1921

This is a case that really helps us see how different these things are.1929

AB is not equal to BA in a single one of its entries; we get totally different things.1934

So, the order of multiplication, if you are multiplying from the left or you are multiplying from the right--that really, really matters.1940

And that is going to affect how we pay attention to doing matrix algebra in the next two lessons.1945

That is something to think about later on.1949

But for right now, you just have to be aware that AB and BA are totally different.1951

Swapping the order of matrix multiplication means you have to do it again,1955

because you have no idea what is going to come out of it until you actually work through it.1958

All right, finally, we have two special matrices to talk about.1962

First, the zero matrix: the zero matrix is a matrix that has--no big surprise--0 for all of its entries.1966

A zero matrix can be made with any order at all.1973

It is denoted by a 0 as bold; however, if you are writing it by hand, normally you can just tell by writing a zero;1975

and people will know, from context, that that 0 is supposed to be a zero matrix, depending on how the problem is working.1982

But if you really want to denote it, you could probably put some underlines underneath it, or something,1987

to show that it is really important--whatever you want to be able to see that it is definitely a matrix.1991

But for the most part, just writing a 0, if it is next to other matrices...people will know what you are talking about.1997

If you need to show its order, you can write it with a subscript of m x n; that tells us that that zero matrix will have m rows, n columns.2002

So, for example, if we had 3 x 3, then we have 3 rows and 3 columns of nothing but zeroes.2010

If we have 5 x 2, then we have 5 rows and 2 columns of nothing but zeroes.2017

For any matrix A, A - A comes out to be the zero matrix, because each of its entries will be subtracted2023

by it entries again, so each entry will turn into a 0; we get the zero matrix.2029

And also, the zero matrix, times A, equals the zero matrix, which is equal to A times the zero matrix.2033

So, the zero matrix, multiplying on some other matrix, by the left or the right, turns it into the zero matrix.2039

The zero matrix, through multiplication, crushes other matrices into the zero matrix.2045

All right, finally: the identity matrix: the identity matrix is a square matrix2050

(it is always going to be a square) that has 1 for all of its entries on the main diagonal, and 0 for other entries.2056

It can be any order, as long as it is a square.2064

It is denoted with the symbol I; so you just write that out like a normal capital I.2067

If you need to show what its order is (and remember, its order is going to have to be n x n,2073

because it has to have the same number of rows and columns; we can't have different numbers there),2077

we can use just I with a subscript of n, because we don't have to say n x n,2080

because it has to be square, so we just use one number, one letter.2084

So, if we want to talk about I2, then that would be a 2 x 2 matrix with 1's on the diagonal, and 0's everywhere else.2088

If we want to talk about I5, the identity matrix as a 5 x 5, then that is 1's on this main diagonal,2099

from the top left down to the bottom right; and it is going to be 0's everywhere else on the thing.2104

Why is this identity matrix useful? For any matrix A, any matrix at all, as long as they match in orders appropriately,2116

and there is always going to be some identity matrix that will match up appropriately with any given matrix,2123

identity matrix A is equal to A, and A times the identity matrix is equal to A.2128

The identity matrix, multiplied from the left, or the identity matrix, multiplied from the right, comes out to be2134

just whatever matrix we had started with that wasn't the identity matrix.2140

The identity matrix effectively works the same as multiplying a real number by 1.2143

5 times 1 just comes out to be 5; -20 billion times 1 just comes out to be -20 billion.2148

The identity matrix works the same way: I times A just comes out to be A; I times C just comes out to be C.2153

So, whatever matrix we have, we multiply by the identity matrix; it is the multiplicative identity.2161

It just leaves it as it normally was; it leaves its identity alone--it leaves it the same.2166

All right, we are ready for some examples.2172

First, a little bit of scalar multiplication: let's do the scalar multiplication, and then we will do the subtraction or addition.2174

2 times 5, -7, 2, 11, 3, 4; its order stays the same, so 2 times 5 is 10; 2 times -7 is -14;2180

2 times 2 is 4; 2 times 11 is 22; 2 times 3 is 6; and 2 times -4 is -8.2189

So, at this point, I am going to change this into a plus, and I am going to say that we had -3 here.2196

+ -3 times something is the same thing as -3 times something.2202

We can pull that negative out and put it on the scalar instead.2205

We do that here: -3 times 3 gets us -9; -3 times -2 gets us +6; -3 times 2 gets us -6; -3 times 6 gets us -18; -3 times 0 gets us 0; -3 times -5 gets us +15.2209

At this point, we are ready to combine them: we combine the two things together.2224

We do it based on location: so 10 and -9 will go in the first row, first column, because they came from the first row and first column.2229

10 and -9 gets us 1; it is going to have the same order here.2235

-14 and 6 gets us -8; -6 and 4 gets us -2; 22 and -18 gets us +4; 6 and 0 gets us +6; -8 and 15; and we have 7; and there is our matrix.2239

All right, now we could have done this a different way.2262

At this point up here, we chose to do plus onto a negative scale, but we could have left it with subtraction.2265

If we had chosen to leave it as subtraction, our first matrix would have remained the same: 10, -14...2272

still the same scalar, so nothing is going to change here from that first matrix.2279

And now, it is going to be minus...we could multiply that scalar by it instead.2283

So, we are going to leave it as a subtraction, but we are just going to multiply that +3 as if it wasn't changed over.2288

So, 3 times 3 gets us 9; 3 times -2 gets us -6; 3 times 2 gets us +5; 3 times 6 gets us +18; 0; and -15.2297

All right, notice: the only difference between these two matrices is this negative sign having hit everything.2308

At this point, we can subtract, and we would end up having 10 - 9; 10 - 9 comes out to be 1.2315

-14 - -6; well, - -6 becomes + 6; -14 + 6 becomes -8; 4 - 6 is -2; 22 - 18 is 4; 6 - 0 is 6; -8 - -15 becomes + 15; -8 + 15 becomes +7.2323

So, we end up getting the exact same thing.2343

Whichever way we do it ends up coming out to be the same thing, which is what we had hoped.2345

I would, for the most part, recommend doing this method that I did here, where you make it addition, and you put the negative on the scalar.2350

You swap it from being subtraction to addition, and then you put the negative on the scalar.2359

And then, you multiply that through, because it gives you one less thing to have to keep track of,2364

as opposed to having to remember the entire time, "I am subtracting; I am subtracting; I am subtracting,"2367

because then, if you forget to subtract just once, your answer is gone; you now have the wrong answer.2371

But if you put the negative on it there, then you remember to multiply by the negative the whole time through.2375

And then, from there, it is just addition.2380

I think it is easier that way; but if you think it would be easier by doing subtraction, go ahead and do that.2382

Whatever works best for you is what you want to use.2386

But I personally would recommend multiplying by the negative, and then doing addition, as opposed to keeping around subtraction.2388

But they will both work just fine.2394

The next example: A is this matrix; B is this matrix; C is this matrix; if the matrix multiplication below is possible,2396

give the order, the size, of the matrix that it would result in.2402

So, we have AB times B...OK, to do that, the first thing we are going to have to do is talk about what each one of these sizes are.2406

If we have 3 rows, 2 columns, that is a 3 x 2 matrix for A.2414

B is 2 columns, 3 rows, so that is a 2 x 3 matrix for B.2420

And C is 3 rows, 3 columns; it is square, so we have a 3 x 2 matrix here.2426

Great; so at this point, it is a question of comparing--do these things match up?2433

AB is going to be 3 x 2, multiplying against a matrix that is 2 x 3.2436

To do this, we have to have...the first one's number of columns has to match the second one's number of rows.2450

But an easier way to do this is to just think in terms of the inner numbers.2455

Are the inner numbers the same? Well, the inner numbers are both 2; so now, what is going to result is the outer numbers.2458

We get those outer numbers as the resultant size of the matrix; so we will get a 3 x 3 matrix in the end.2464

If we reversed this and looked at B times A, then we would have a 2 x 3 matrix times a 3 x 2 matrix.2470

We check: are the inner numbers the same? 3 and 3 are the same, so it becomes the outer numbers; those will be our resultant.2477

So, we will get a 2 x 2; so notice, AB and BA are very different in the end.2483

And we can see that, just based on the fact that they have totally different orders.2487

So, you can end up getting different sizes, as well, based on it.2490

Not only are they not commutative (we can't rely on AB being the same thing as BA); we can't even rely on the size remaining the same.2493

Next, let's look at C on B: that is a 3 x 3 times a 2 x 3; so in this case, do they match up?2503

Does 2 match up with 3--are they the same number?2515

No, they don't match up; so we have no solution here.2517

A on C: a 3 x 2 matrix multiplied with a (sorry, I need to switch to green) 3 x 3 matrix--do they match up?2520

3 and 2 don't match up; so we don't have anything that comes out of that, as a result.2534

And finally, CAB: well, can we multiply multiple matrices together?2539

Sure--we do one matrix multiplication; that comes out as another matrix; and then just multiply the resultant thing.2543

So, let's see if we can work through this: CAB is a 3 x 3, multiplied by a 3 x 2, multiplied by a 2 x 3.2548

So, our first question that we want to do...let's work from the left to the right.2560

So, we will look at what CA became, and then we will multiply by B.2563

So, CA...we have a 3 here; we have a 3 here; so that is going to result in a 3 x 2 (that is what CA would come out as),2567

times B still (we have to do B), 2 x 3; so now we ask--do they match on the inside?2576

They match on the inside, so what is going to result is a 3 x 3; there is our answer.2582

Let's also look at it if we had gone from another direction--if instead of going from the left, we came from the right.2588

We would hope that that would work out, because if it didn't, then there are some issues with how we have this stuff set up.2591

So, let's look at CAB, if we had done CAB from the right side to the left.2596

We have the same thing: a 3 x 3 is C; A is 3 x 2; and B is a 2 x 3.2605

So now, we are working from the right side; so what does AB come out to be?2613

Well, that is a 2 here and a 2 here, so that comes out to be 3.2617

And look, we already did this--we already figured out what AB is.2619

We know that that should come out to be a 3 x 3; so from there, we have a 3 x 3 matrix.2622

And then, what came out of a AB is a 3 x 3 matrix; the 3's match up, so what we get in the end is a 3 x 3 matrix.2628

So, that checks out; either way we did it, it is the same.2638

One last thing to point out here: look, if we had a 3 x 3, if we had CAB one more time...a 3 x 2, and then a 2 x 3...2641

well, what we can do is say, "Do the inner parts match?"2654

The inner part here matches, and the inner parts here match.2658

Ultimately, what is going to happen is that all of the inner parts are required to match for multiplication to happen.2661

But they all disappear; the only thing that ends up making it out in the end is things on the far edges, the 3 and 3 on both sides.2665

So, what is going to come out in the end is a 3 x 3.2674

So, if you have multiple matrices multiplying against each other, you can just check and make sure that all of the inner numbers match up against each other.2677

And then, the size of the resulting thing will end up just being the far edge numbers, which were, in this case, 3 and 3.2683

All right, the next example is a big, big one of matrix multiplication.2691

We are going to work to simplify this, so first, let's see what size our product is going to come out to be.2695

So, we have a 3 x 3 and a 3 x 3; it is possible--no surprise there, since it was given to us as a problem.2700

That is going to come out as a 3 x 3 matrix.2705

At this point, let's work it out; since we are working with a 3 x 3 matrix, we will leave a nice, big chunk of space for us to work inside of.2709

So, we are going to work this out: the first row times the first column will get the number that is going to go2716

in our first row, first column of our resultant product matrix.2726

So, 6 (the first entry of the first row), times the first entry of the first column, -2, added to 22730

(the second entry of the first row), times the second entry of our first column, plus 3 times the final entry,2739

the third entry, of our first row, times the final of entry, the third entry, of our first column:2749

we work that out; we get -12 + 8 - 3; we get -15 + 8, so we get -7.2754

We have -7 for the entry--that first row, first column entry.2765

That is what is going on behind the scenes.2769

We are taking that row; we are taking that column; we are multiplying them based on how the entries multiply together,2772

matching the entries, multiplying matching entries, and then summing up the whole thing.2778

We can see that as I just wrote it out there.2781

So, clearly, this takes a lot of arithmetic: we are doing three multiplications and three additions--it is tough to do this.2785

I would recommend, if you aren't excellent at doing mental math: really try to keep some scratch paper that you are doing on the side.2791

Be very, very careful working with your calculator; it is so easy to make mistakes in matrix multiplication,2798

Especially the first couple of times you are doing it--it is something you really have to be careful with the first couple of times.2803

And it is something you always have to be careful with, because you can always easily make mistakes.2807

Even I will very easily make mistakes with matrix multiplication.2811

But if you just stay focused and pay attention to these rules carefully, and you work carefully,2814

you can always make sure that you get the answer right.2819

But just really be careful with matrix multiplication.2821

It is an easy place to make simple mistakes where you understand what is going on,2823

but you just made a little arithmetic error, and it makes your answer wrong; so be careful.2827

All right, I am going to do the rest of these by just working through them in my head and talking them out,2831

because I am pretty good at this; but be careful when you are doing it.2835

If you are not really good at doing this sort of stuff in your head, be careful; do it on scratch paper.2839

And the larger the matrices get, the harder it is to do in your head.2843

So, 6, 2, 3 on 1, 2, 0; the first row on the second column: 6 times 1 is 6; 2 times 2 is 4; 6 + 4 is 10.2845

3 times 0 is 0; so we get 10.2854

And then the final column: 6, 2, 3 on -3, 0, 1: 6 times -3 is -18; 2 times 0 is 0, so still -18; 3 times 1 is 3; so -18 + 3 is -15.2857

The next one (let's switch to a new color): second row: 1, 0, -8; multiply that by the first row;2868

so, second row, first column, is going to be 1, 0, -8 on -2, 4, -1; 1 times -2 is -2; 0 times 4 is 0;2874

so, -2 + -8 times -2 becomes +8, so -2 + +8 becomes +6.2881

The next one: 1, 0, -8 on 1, 2, 0; 1 times 1 is 1; 0 times 2 is 0; -8 times 0 is 0; so we just get 1 out of that.2890

1, 0, -8 on -3, 0, 1; 1 times -3 is -3; 0 on 0 is 0; -8 times 1 is -8; so -3 + -8 becomes -11.2901

And then finally, -7, 3, 5 on -2, 4, -1: -7 times -2 becomes +14; 3 times 4 becomes +12; 14 + 12 becomes +26;2911

5 times -1 is -5; 26 - 5 is 21; OK; -7, 3, 5 on 1, 2, 0: -7 times 1 is -7; 3 times 2 is 6; -7 + 6 is -1; and then + 5 times 0,2927

so we just come out to be -1 here.2942

And then the final one: -7, 3, 5, on -3, 0, 1: -7 times -3 becomes +21; 3 times 0 is 0; +21 still; plus 5 times 1 is 5; 21 + 5 is 26.2944

OK, so hopefully, that points out just how much arithmetic you are having to do in your head here.2960

I really want you to be careful, because this is the easiest way to make mistakes.2967

And it is the pretty silly way to end up losing points on a test or homework,2970

because it is not because you don't understand what is going on.2973

It is just because you are trying to do so much arithmetic in your head; it is easy to make a mistake.2975

If you end up having any difficulty with something particularly hard, just write it out on paper.2979

Or if you have a really nice graphing calculator, where you can see each of the numbers you are putting into it,2983

be careful; watch; make sure that what you have there matches up to what you have on the paper.2987

And then, make sure that you are being careful if you are using a calculator.2991

So, just be careful, however you are approaching it.2994

The actual process isn't that difficult, once you get used to it; but it is always going to be a real chance of making mistakes,2996

just because there is so much arithmetic going on.3002

All right, the final example: Prove that for any 2 x 2 matrix A, the zero matrix times A equals 0, and the identity matrix times A equals A.3004

So, any 2 x 2 matrix A--it says "any," so that means we can't just use some 2 x 2 matrix.3012

We can't actually put down numbers, because we have to be able to have this true for any 2 x 2 matrix.3018

So, if we came up with some matrix, like 3, 1, 7, 47; well, maybe it happens to be true for that matrix.3023

So, we have to figure out a way to be able to show that it is true for every matrix.3031

We need to write about this in a general form, so we do the same thing that we have everything set up in, where we use variables,3034

because if we have it as a, b, c, d, then any 2 x 2 matrix...we can just swap out a, b, c, d for actual numerical values,3039

and we will have any 2 x 2 matrix--this will be true for any 2 x 2 matrix.3049

So, we have that as an idea; so now we can just try multiplying.3052

If this is going to work for this A here, a, b, c, d, that is a stand-in for any 2 x 2 matrix at all.3056

So, if we can show that the zero matrix times this comes out to be 0 anyway, then it has to be true for all of them,3063

because they are just going to end up swapping out the variables, a, b, c, d, for actual numbers.3068

So, let's try this: the zero matrix times A = 0: well, if we have the zero matrix, then we are going to have 0, 0, 0, 0,3072

because it is multiplying against a 2 x 2; our A is a, b, c, d.3079

This is actually pretty easy matrix multiplication, thankfully.3084

0 and 0 times a and c; look, that is going to crush it down to 0.3087

We know that we are going to have to get a 2 x 2 matrix, because we started with 2 x 2 times 2 x 2.3091

0, 0 on b, d: well, 0 times b and 0 times d--that comes out to be 0.3095

0, 0 on a, c: 0 times a and 0 times c--that comes out to be 0.3100

0, 0 on b, d: 0 times b and 0 times d--no surprise there--comes out to be 0.3103

So, we see, because it has nothing but zeroes that we are multiplying by:3108

whatever it is going to hit is going to get turned into a 0.3111

So, that is why the zero matrix, multiplied by any matrix at all, ends up coming out to be the zero matrix.3113

So, this checks out: and we can see that, if we had put it as the zero matrix,3119

multiplying from the right as opposed to the left, basically the same thing is going to happen,3123

because we are multiplying against, it is going to hit nothing but zeroes, so it is just going to get turned to 0's automatically.3126

So, the zero matrix, multiplied against any matrix, becomes the zero matrix.3132

Next, the identity matrix times A: let's show that that becomes A.3136

The identity matrix: well, A is a 2 x 2, so that means that our identity matrix will have to also be a 2 x 2.3140

So, 1's are on the main diagonal, with 0's everywhere else.3145

1, 0, 0, 1; the main diagonal has 1's, and then everything else will have 0's (in this case, not many 0's).3149

a, b, c, d: this will take a little bit more thought.3156

1, 0 on a, c: 1 times a...we are going to have to get a 2 x 2, because we started with 2 2 x 2 matrices...3160

1 times a comes out to be a; 0 times c becomes 0; so we have a.3170

1, 0 on b, d: well, 1 times b comes out to be b; 0 times d comes out to be 0, so we get just b.3175

0, 1 on a, c: 0 times a comes out to be 0; 1 times c comes out to be c.3183

0, 1 on b, d: 0 times b comes out to be 0; 1 times d comes out to be d.3189

That checks out; it ended up being the same thing.3194

So, it is a little bit harder to see why the identity matrix is working;3196

but basically, what it is doing is: when you multiply some other matrix by it,3199

since it has just a 1 in one place, it is seeing what is at the same location over here.3204

a is at the same location; b is at the same location; so they end up popping out.3209

For this one down here, 0 and 1, it is seeing what is at the same location: c and d are there, so they get to pop out, as well.3213

A similar thing ends up happening if you multiply the identity matrix from the right, instead of from the left.3219

Try it out for yourself--take a look, if you are curious about seeing it.3223

All right, that shows everything that we have.3226

We have a pretty good understanding of how matrices work at this point.3228

We are ready to go and see some of the cool things that we can start doing with them.3230

So, we will talk about some new ideas in the next section, the next lesson.3233

And then two lessons from now, we will see just how powerful these things can be3236

for solving problems that would seem really difficult; we are going to turn them so easy so quickly.3240

All right, we will see you at Educator.com later--goodbye!3245