Join Prof. Sandahl Nelson in her time-saving AP Statistics course that combines clear explanations of concepts along with tons of live AP-level examples. Prof. Nelson also covers how to use popular TI graphing calculators for analysis in class and during the exam.

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## I. Describing Data: Graphically & Numerically

Constructing & Interpreting Graphs 37:14
Intro 0:00
Objectives 0:08
Categorical Data 0:26
Pie Charts 0:27
Bar Graphs 1:20
(More) Bar Graphs 2:25
Comparative 2:26
Relative Frequency 3:30
Numerical Data: Discrete 4:35
Dot Plots 4:36
Stem and Leaf Plots 6:08
Example: Stem Plot 7:55
Example: Stem Plot 7:56
Numerical Data: Continuous 9:03
Numerical Data (Continuous) 9:04
Example I: Histogram 10:57
Numerical Data: Cumulative Frequency Plots 16:49
Frequency Polygon 16:50
Ogive Plot 18:00
Describe the Distribution 19:42
SOCS: Shape, Outlier, Center, Spread 19:43
Shape 20:28
Unimodal, Bimodal, or Multimodal 20:29
Symmetric Distribution 21:48
Positively Skewed Distribution 21:30
Negatively Skewed Distribution 21:46
Example II: Describe the Distribution 22:06
Stem Plots to Compare Two Groups of Data 23:06
Stem Plots to Compare Two Groups of Data 23:06
Example III: Compare the Distribution 23:47
Example IV: Describe the Distribution of Quiz Scores 27:45
Example V: Stem Plot 29:26
Example VI: Bar Graph & Relative Frequency 30:53
Summarizing Distributions of Univariate Data 1:07:37
Intro 0:00
Objectives 0:10
Measuring Center 0:42
Median 0:43
Mean 0:56
Example: Find the Median and Mean 1:59
Measuring Position 1:59
Percentiles 6:58
Quartiles 7:39
Example: Find the Quartiles 8:58
Range 11:14
IQR 11:33
Variance 11:55
Example: Find the Measures of Spread 22:09
Outliers 27:23
Outliers 27:24
Example: Outliers 29:05
Boxplots 31:44
5-number Summary 31:45
Example I: Boxplot 33:55
Describe the Distribution 44:20
SOCS: Shape, Outlier, Center, Spread 44:21
Example II: Describe the Distribution 46:08
The Effect of Changing Units on Summary Measures 48:26
Linear Transformations 48:27
Example: Distribution of Ages 50:42
Example III: Modified Boxplot & Describe the Distribution 53:26
Example IV: Describe the Distribution 62:37

## II. Correlation & Regression

Correlation & Regression 50:16
Intro 0:00
Objectives 0:07
Scatterplots 0:30
Scatterplots 0:31
Interpreting Scatterplots 2:20
Direction 2:34
Form 2:50
Strength 3:29
Example: Describe the Direction, Form, and Strength of the Scatterplot 4:00
Correlation Coefficient ( r ) 5:22
Correlation Coefficient ( r ) 5:23
Example: Correlation Coefficient ( r ) 7:52
Approximate the Correlation Coefficient 7:53
Interpret the Correlation Coefficient 8:48
Least Squares Regression Line (LSRL) 9:23
Least Squares Regression Line (LSRL) 9:24
Interpreting the LSRL 10:45
y-intercept, Slope, Mean, and SD 10:46
Example: Interpreting the LSRL 14:48
Step 1: Determine the Least-squares Regression Line 14:49
Step 2: Interpret the Slope and y-intercept of the Regression Line 18:28
Step 3: Interpret the Correlation 20:56
Coefficient of Determination 23:50
RÂ² = (r)Â² 23:51
Residuals 26:04
Residual = Observed y - Predicted y 26:05
Residual Plot 27:04
Example: Calculate the Residual 28:33
Example: Draw the Residual Plot 31:18
Example I: Explanatory Variable & Response Variable 37:47
Example II: Find the Least-squares Regression Line 39:08
Example III: Calculate the Residual 44:10
Example IV: Predicted Value 47:50
Example V: Residual Value 49:28
Regression, Part II 23:26
Intro 0:00
Objectives 0:10
Outliers and Influential Points 0:20
An OUTLIER 0:21
Influential Observations 1:05
Transformations to Achieve Linearity 2:39
Transformations to Achieve Linearity: When We Need It 2:40
Transformations to Achieve Linearity: How We Use It 4:41
Example I: Expected Number of Sales 7:11
Confounding 11:13
Confounding 11:14
Correlation Does NOT Prove Causation 11:55
Correlation Does NOT Prove Causation 11:56
Lurking Variables 13:06
Lurking Variables & Common Response 13:07
Confounding 14:25
Confounding 14:26
Example: Promotion to Increase Movie Sales 15:11
Example II: Causation, Confounding, or Common Response 16:26
Example III: Correlation 18:25
Example IV: Confounding & Common Response 19:50

## III. Surveys & Experiments

Planning & Conducting Surveys 29:35
Intro 0:00
Objectives 0:09
Census vs. Survey, Parameter vs. Statistics 0:28
Census vs. Survey, Parameter vs. Statistics 0:29
Characteristics of a Well-Designed and Well-Conducted Survey 2:15
Representative Sample 2:16
Random Sample 3:38
Does Not Introduce Bias 4:02
Bias 4:16
What Is It? 4:17
How Might It Occur? 5:26
Example I: Identify the Type of Bias 7:03
Random Sampling 10:25
Simple Random Sample (SRS) 10:26
Example II: Random Sampling 13:26
Random Sampling, Cont. 16:44
Stratified Random Sampling 16:55
Cluster Sample 18:06
Systematic Random Sample 19:16
Example III: Random Sampling 20:52
Non-Random Sampling 22:28
Convenience Sample 22:29
Voluntary Response Sample 22:54
Example IV: Sampling Design 25:01
Specify The Population 25:02
Describe The Sampling Design. Will You Use a Stratified Sample? 26:46
Planning & Conducting Experiments 41:31
Intro 0:00
Objectives 0:09
Experiments vs. Observational Studies 0:44
Observational Study 0:45
Experiment 1:28
Example I: Experimental or Observational? 2:09
Example II: Experimental or Observational? 2:57
Placebo Effect 3:51
Placebo Effect 3:52
Characteristics of a Well-designed and Well-conducted Experiment 4:42
Control 4:43
Replicate 5:32
Randomize 6:32
Example III: Control Groups 7:33
Completely Randomized Design 9:01
Completely Randomized Design 9:02
Outline/Map of Completely Randomized Design 9:55
Outline/Map of Completely Randomized Design 9:56
Example IV: Completely Randomized Design 11:35
Block Randomization 14:23
Block Randomization 14:24
Randomized Block Design 15:29
Randomized Block Design 15:30
Example V: Randomized Block Design 18:06
Matched Pairs Design 21:08
Matched Pairs Design 21:09
Example V: Types of Experiments 22:42
Example VI: Types of Experiments 24:17
Example VII: Types of Experiments 26:24
Experimental Set Up 28:28
Treatment 28:29
Experimental Units 29:13
Response 29:32
Double-blind Experiment 31:06
Double-blind Experiment 31:07
Example VIII: Double-blind Experiment 32:37
Example IX: Design a Study to Test Hypothesis 37:04
Generalizability of Results 40:39
Statistically Significant Data 40:40

## IV. Probability & Expected Value

Probability Overview 1:22:17
Intro 0:00
Objectives 0:21
Interpreting Probability 0:46
Probability of a Random Outcome or the Long Term Relative Frequency 0:47
Law of Large Numbers 1:42
Expected Value 1:43
Example I: Probability in Poker 2:21
Probability Model 4:31
Sample Space (S) 4:32
Event 5:15
Probabilities 6:03
Example II: Basketball Free Throws 6:37
Part 1: Sample Space 6:46
Part 2: Event 8:08
Part 3: Probability 8:48
Disjoin Events (aka Mutually Exclusive) 11:00
Disjoin Events (aka Mutually Exclusive) 11:01
Part A: Venn Diagram 12:24
Probability of Disjoin Events 14:03
Probability of Disjoin Events 14:04
Example IV: Probability of Disjoin Events 15:58
Independence vs. Dependence 18:11
Independence vs. Dependence 18:12
Example V: Independence vs. Dependence 20:26
Example VI: Independence vs. Dependence 22:23
Probability Rules 23:13
Probability Rules 23:14
Probability Notation 23:31
P (A or B) 23:32
P (A and B) 23:58
P ( A given B happened) 24:24
P ( not A) 24:44
Example VII: Probability Notation 25:17
Probability Rule Notation 26:49
A or B 26:50
A and B 27:40
Example VIII: Determine if These Two Events are Independent 29:05
Example IX: Conditional Probability of Wining 31:39
Example X: Conditional Probability of Students 36:46
Part A: Probability 36:47
Part B: Conditional Probability 38:18
Part C: Conditional Probability 39:59
Example XI: Conditional Probability of Children 42:53
Part A: All Boys 42:54
Part B: All Girls 44:44
Part C: Exactly Two Boys or Exactly Two Girls 45:50
Part D: At Least One Child of Each Sex 50:18
Overview 52:52
Complement 52:53
Mutually Exclusive 53:30
Intersection 53:49
Union 54:44
Independent 55:34
Bayes Rule 56:02
Bayes Rule 56:03
Example XI: Probability & Bayes Rule 59:43
Example XII: Probability & Bayes Rule 67:49
Simulations 65:46
Simulations 65:47
Example XIII: Simulations 67:10
Intro to Probability for Discrete Random Variables 31:37
Intro 0:00
Objectives 0:09
Discrete vs. Continuous Random Variables 0:29
Discrete Random Variables 0:30
Continuous Random Variables 1:12
Probability Distribution 3:36
Probability Distribution for a Discrete Random Variables 3:37
Probability Rules 4:20
Example I: Find the Probability 4:51
Example II: Construct a Probability Distribution 6:15
Mean 9:35
Expected Value 9:36
Example: Expected Number of Customers 10:08
Variance 13:19
Variance 13:20
Example: Variance 14:34
Example III: Probability Analysis 18:01
Example IV: Expected Profit 25:25
Discrete Random Variables 39:06
Intro 0:00
Objectives 0:08
Binomial Distribution 0:14
BINP 0:15
B 0:34
I 0:49
N 1:00
P 1:20
Example I: Binomial Distribution 1:43
Question 1: Is a Binomial Distribution a Reasonable Probability Model for the Random Variable X? 1:44
Question 2: Is a Binomial Distribution a Reasonable Probability Model for the Random Variable X? 3:43
Binomial Probability 5:11
Binompdf (n, p, x) 5:12
Example II: Determine the Probability 10:37
Part A: Determine the Probability that Exactly One of the Toasters is Defective 10:38
Part B: Determine the Probability that At Most Two of the Toasters are Defective 16:40
Part C: Determine the Probability that More Than Three of the Toasters are Defective 21:42
Geometric Distribution 24:11
Geometric Distribution 24:12
Example III: Geometric Distribution & Probability 25:14
Part A: Geometric Distribution 25:15
Geometric Probability 26:55
Geometpdf (p, x) 26:56
Example III: Geometric Distribution & Probability 27:50
Part B: Geometric Probability of Exactly Four Patients 27:51
Part C: Geometric Probability of At Most Five Patients 31:19
Mean and SDs 33:47
Binomial 33:48
Geometric 34:28
Example IV: Defective Units 34:53
Example V: Number of Patients 35:58
Combining Independent Random Variables 18:56
Intro 0:00
Objectives 0:09
Mean and Standard Deviation of Two Random Variables 0:26
Mean and Standard Deviation of Two Random Variables 0:27
Example I: Average and Standard Deviation 1:58
Example II: Average and Standard Deviation 4:37
Transforming Random Variables: “Linear Transformations” 6:10
Transforming Random Variables: “Linear Transformations” 6:11
Example III: Mean and Standard Deviation 7:02
Example IV: Mean and Standard Deviation 10:23
Example V: Mean and Standard Deviation 14:14
Part 1: Mean & SD 14:15
Part 2: Mean & SD 16:30
Normal Random Variables 59:34
Intro 0:00
Objectives 0:08
The Empirical Rule 0:28
68% 0:29
95% 1:43
99.70% 2:00
The Empirical Rule, Cont. 2:31
The Empirical Rule, Cont. 2:32
Example I: The Empirical Rule 3:24
Z-Score 8:17
Z-Score 8:18
Example II: Z-Score 10:08
Using the Normal Table 13:03
Using the Normal Table 13:04
Using the Normal Table, Cont. 15:05
Example III: Using the Normal Table and Z-score to Calculate Probability 16:01
Step 1: Sketch 16:02
Step 2: Calculate Z-score 18:16
Step 3: Solve for Probability Using the Normal Table 19:14
Example IV: Using the Normal Table and Z-score to Calculate Probability 20:29
Step 1: Sketch 20:30
Step 2: Calculate Z-score 21:52
Step 3: Solve for Probability Using the Normal Table 22:36
Example V: Using the Normal Table and Z-score to Calculate Probability 27:20
Step 1: Sketch 27:42
Step 2: Calculate Z-score 28:14
Step 3: Solve for Probability Using the Normal Table 29:45
Example VI: Using the Normal Table and Z-score to Calculate Probability 34:00
Step 1: Sketch 34:01
Step 2: Calculate Z-score 35:48
Step 3: Solve for Probability Using the Normal Table 36:56
Example VII: Using the Normal Table and Z-score to Calculate Probability 41:21
Step 1: Sketch 41:22
Step 2: Calculate Z-score 44:15
Step 3: Solve for Probability Using the Normal Table 47:26
Example VIII: Calculate the Standard Deviation of the Random Normal Variable 49:54
Step 1: Sketch 49:55
Step 2: Calculate Z-score 51:16
Step 3: Solve for Standard Deviation 53:16
Example VIII: Calculate the Mean of the Distribution 55:11
Step 1: Sketch 55:12
Step 2: Calculate Z-score 56:36
Step 3: Solve for Mean 57:42

## VI. Distribution of Data

Sampling Distributions 38:27
Intro 0:00
Objectives 0:07
Parameter vs. Statistics 0:25
Parameter vs. Statistics 0:26
Sampling Distribution 2:03
Sampling Distribution 2:04
Central Limit Theorem 3:15
Central Limit Theorem 3:16
Central Limit Theorem, Cont. 7:23
Example I: Sampling Distribution Graph 9:20
Conditions (RIN) 11:12
Random 11:13
Independent 12:04
Normal 13:40
Sampling Distribution of a Sample Mean 15:19
Sampling Distribution of a Sample Mean 15:20
Example II: Calculate the Mean and SD of a Sampling Distribution 17:17
Sampling Distribution of a Sample Proportion 21:07
Sampling Distribution of a Sample Proportion 21:08
Example III: Mean, SD, Sample Size, and Probability of a Sampling Distribution 22:29
Part A: Calculate the Mean and SD of a Sampling Distribution 22:30
Part B: Sample Size 26:18
Part C: Probability 29:30
Example IV: Probability of a Sampling Distribution 33:40
Part A: Probability of a Random Selection 33:41
Part B: Probability of the Mean 35:46

## VII. Statistical Inference

Confidence Intervals 56:37
Intro 0:00
Lesson Overview 0:07
Why Calculate a Confidence Interval? 0:28
Using a Statistic to Estimate a Parameter 0:29
What is a Confidence Interval? 1:24
Confidence Interval 1:25
General math Behind a Confidence Interval 2:51
Point Estimate 2:52
Critical Value 4:34
Z-Table 6:06
Z-Table 6:07
T-Table 7:07
T-Table 7:08
General math Behind a Confidence Interval 7:50
Point Estimate 7:51
Critical Value: Mean & Proportion 8:00
Standard Error: Mean & Proportion 8:15
Steps to Calculating a Confidence Interval 12:09
Step 2: Check Your Conditions 12:58
Step 3: Calculate 15:33
Step 4: Interpret 16:12
Example I: Confidence Interval 16:29
Example II: Confidence Interval 29:57
Example III: Confidence Interval 42:31
Hypothesis Testing 1:12:16
Intro 0:00
Lesson Overview 0:07
Why do a Hypothesis Test? 0:29
Using a Statistic to Test a Claim about a Parameter 0:30
Steps for Calculating a Hypothesis Test 1:13
1. Write the Hypothesis 1:14
2. Check Conditions 1:30
3. Calculate the Test Statistic 1:34
4. Look Up the P-value & Interpret 1:49
5. Interpret 1:50
Example I: Hypothesis Testing Step by Step 2:57
1. Write the Hypothesis 5:04
2. Check Conditions 8:43
3. Calculate the Test Statistic 21:54
4. Look Up the P-value 20:07
5. Interpret 23:45
Example II: Hypothesis Testing Step by Step 28:49
1. Write the Hypothesis 28:50
2. Check Conditions 32:00
3. Calculate the Test Statistic 34:20
4. Look Up the P-value 38:26
5. Interpret 40:49
Example III: Hypothesis Test for a Mean 44:53
Example IV: Hypothesis Test for a Proportion 57:26
The T Distribution 41:40
Intro 0:00
Lesson Overview 0:07
When Do We Use the T Distribution 0:26
When Do We Use the T Distribution 0:27
What is the T Distribution? 1:46
What is the T Distribution? 1:47
Confidence Interval Example 2:49
Construct and Interpret a 90% Confidence Interval to Estimate the Mean 2:50
Hypothesis Test Example 16:59
1. Write the Hypothesis 17:00
2. Check Conditions 20:01
3. Calculate the Test Statistic 21:24
4. Look Up the P-value 24:39
5. Interpret 27:23
Matched Pairs T-test 29:34
Matched Pairs T-test 29:35
1. Write the Hypothesis 33:05
2. Check Conditions 34:58
3. Calculate the Test Statistic 35:52
4. Look Up the P-value 38:12
5. Interpret 39:28
Two Samples 1:27:23
Intro 0:00
Lesson Overview 0:09
What Will a 2 Sample Problem Look Like? 0:40
Example 1 0:41
Example 2 2:01
Hypothesis Test Example I 7:02
1. Write the Hypothesis 7:03
2. Check Conditions 10:04
3. Calculate the Test Statistic 13:21
4. Look Up the P-value 20:54
5. Interpret 22:48
Hypothesis Test Example II 24:50
1. Write the Hypothesis 24:51
2. Check Conditions 28:34
3. Calculate the Test Statistic 29:46
4. Look Up the P-value 36:27
5. Interpret 39:01
Example I: Two Samples Hypothesis Testing 42:11
Example II: Two Samples Hypothesis Testing 53:30
Example III: Reliability Testing 78:31
Hypothesis Testing of Least-Squares Regression Line 53:49
Intro 0:00
Lesson Overview 0:10
Review of Least-squares Regression and Interpretation 0:29
Correlation Coefficient ( r ) 0:30
Equation of the Least-squares Regression Line 1:02
Example 2:45
Part A: Least-squares Regression Line 2:46
Part B: Slope of the Least-squares Regression Line 6:03
Test for the Regression Line 7:50
Is There a Correlation? 7:51
Is the y-intercept = 0? 9:56
Conditions for Hypothesis Testing 10:49
Linearity 11:27
Constant Variability 12:35
Normality 13:40
Independence 15:16
Hypothesis Testing 16:10
Standard Deviation of the Residuals 16:11
Standard Error of Slope 17:30
Test Statistic 18:45
Confidence Interval 19:36
Example: Hypothesis Testing 20:45
Part A: Test the Hypothesis 20:46
Part B: 95% Confidence Interval of the Slope 32:51
Interpreting Computer Output 35:40
Interpreting Computer Output 35:41
Example I: Interpreting Computer Output 38:46
Part A: Least-squares Regression Equation 38:47
Part B: Standard Error 40:01
Part C: Slope of the Least-squares Regression Line 41:21
Part D: Null and Alternative Hypotheses 42:08
Part E: Value of Test Statistic 43:09
Part G: P-Value 44:03
Part H: Is Income Useful for Predicting the Cost of a Person’s Car? 45:46
Part I: Estimated Cost 46:57
Example II: Interpreting Computer Output 47:48
Hypothesis Tests for Categorical Data (Chi-Squared Tests) 1:12:55
Intro 0:00
Lesson Overview 0:11
How Do We Know to Use a Chi-Squared Test? 0:27
Categorical Data 0:28
Chi-Squared Goodness of Fit Test 1:50
One Categorical Variable with Counts in Each Category 1:51
What We Have Seen 2:17
New Question Type 2:56
Example I: Chi-Squared Goodness of Fit Test 4:02
Chi-Squared Goodness of Fit Steps Overview 4:03
Step 1: Hypothesis 5:54
Step 2: Expected 7:42
Step 3: Conditions 10:34
Step 4: Calculate 11:44
Step 5: P-Value & Chi-Square Distribution Table 17:03
Example II: Chi-Squared Goodness of Fit Test 22:04
Step 1: Hypothesis 22:05
Step 2: Expected 24:55
Step 3: Calculate 29:05
Step 4: P-Value & Chi-Square Distribution Table 33:18
Chi-Squared Test of: Homogeneity or Independence/Association 34:31
Homogeneity 34:32
Independence/Association 35:42
Example III: Chi-Squared Test of: Homogeneity or Independence/Association 37:55
Step 1: Hypothesis 37:56
Step 2: Expected 40:28
Step 3: Conditions 46:48
Step 4: Calculate 47:49
Step 5: P-Value & Chi-Square Distribution Table 49:30
As a Test of Association 52:53
As a Test of Association 52:54
Example IV: Chi-Squared Test of: Homogeneity or Independence/Association 55:05
Step 1: Hypothesis, Expected, and Conditions 55:06
Step 2: Calculate 59:45
Step3: P-Value & Chi-Square Distribution Table 61:51
Example V: Chi-Squared Test of: Homogeneity or Independence/Association 62:48
Step 1: Hypothesis 62:49
Step 2: Expected and Conditions 65:12
Step 3: Calculate 66:36
Step 4: P-Value & Chi-Square Distribution Table 70:50

## VIII. AP Practice Test

Practice Test 2013 AP Statistics 1:02:57
Intro 0:00
Question 1 0:23
Question 1: Part A 0:24
Question 1: Part B 2:10
Question 2 6:16
Question 2: Part A 6:17
Question 2: Part B 10:22
Question 2: Part C 12:09
Question 3 14:30
Question 3: Part A 14:31
Question 3: Part B 18:19
Question 4 24:49
Question 4: Part A 24:50
Question 5 37:27
Question 5: Part A 37:28
Question 5: Part B 42:32
Question 6 51:15
Question 6: Part A 51:16
Question 6: Part B 55:17
Practice Test 2014 AP Statistics 1:00:07
Intro 0:00
Question 1 0:32
Question 2 9:46
Question 2: Part A 9:47
Question 2: Part B 12:28
Question 2: Part C 13:22
Question 3 15:38
Question 3: Part A 15:39
Question 3: Part B 18:40
Question 4 27:33
Question 4: Part A 27:34
Question 4: Part B 30:05
Question 5 34:15
Question 5: Part 1 34:16
Question 5: Part 2 37:29
Question 5: Part 3 39:50
Question 5: Part 4 40:59
Question 5: Part 5 44:09
Question 6 45:30

## Course Details:

Duration: 15 hours, 4 minutes

Number of Lessons: 18

This online AP Statistics course is not only perfect for high school students enrolled in the class and taking the test, but is also ideal for college students taking general statistics as well. Professor Nelson covers all the topics of the AP Statistics exam with tons of examples, as well as how to use the popular TI graphing calculators in statistical analysis.

• Free Sample Lessons

Topics Include:

• Correlation and Regression
• Planning and Conducting Experiments
• Probability for Discrete Random Variables
• Sampling Distributions
• Confidence Intervals
• Hypothesis Testing
• Least Square Regression Lines
• Chi-Squared Tests