Enter your Sign on user name and password.

Forgot password?
  • Follow us on:

Without Statistics, the type of quantitative reasoning necessary for making important would be nearly impossible. In Educator's AP Statistics course, Dr. Philip Yates teaches you both the theoretical aspects and real-world applications of statistical analysis, along with how to ace the AP test. Professor Yates directs you through difficult concepts with easy to understand examples. He brings Statistics to life by drawing from his love and investigations of sports statistics and environmental science. This course is indispensible to those having difficulty with any topic in statistics ranging from Data Analysis, Probability, and Sampling, to Confidence Intervals and Hypothesis Testing. Along with his strong academic background and enthusiasm, Dr. Yates brings with him over eight years of Statistics teaching experience.

Loading video...
expand all   collapse all
I. Introduction
  Basic Ideas 17:34
   Intro 0:00 
   Basic Definitions 0:09 
    Element (member, unit) 0:20 
    Variable 1:01 
    Observation (measurement) 1:18 
    Data Set 1:40 
   Example: Basic Definitions 1:55 
   Qualitative Variables 4:58 
   Quantitative Variables 6:16 
    Discrete Variable 6:33 
    Continuous Variable 7:36 
   Cross Section vs Time Series Data 8:58 
   Summation Notation 10:50 
   Summation Notation 2 12:59 
   Summation Notation 3 15:32 
II. Exploring Data
  Raw Data, Dotplots, Stemplots 27:24
   Intro 0:00 
   Raw Data 0:07 
    Ungrouped Data 0:25 
    Example: Ages 0:39 
   Features of Graphical Displays of Distributions 1:28 
    Center and Spread 1:54 
    Clusters and Gaps 2:04 
    Outliers (extreme values) 2:12 
    Symmetric 2:48 
    Skewed 3:14 
    Uniform 3:47 
   Dotplots 4:58 
   Example: Dotplots 8:51 
   Stemplot 11:12 
    Stem and Leaf 11:17 
   Example: Stemplot 15:18 
   Extra Example 1 3:48 
   Extra Example 2 4:00 
  Histograms, Cumulative Frequency Plots 10:21
   Intro 0:00 
   Features of Graphical Displays of Distributions 0:07 
   Histogram 3:03 
    Common Programs 3:09 
   Example: Histogram 6:14 
   Cumulative Frequency Plot 7:43 
   Example: Cumulative Frequency Plot 8:16 
  Summarizing Distributions, Measuring Center 16:04
   Intro 0:00 
   Measures of Central Tendency 0:08 
   Mean (average) 0:28 
    Mean for Population Data 0:51 
    Mean for Sample Data 1:18 
   Example: Mean 1:57 
   Example: Mean 2:49 
   Median 3:53 
   Example: Median 4:52 
   Example: Median 6:47 
   Mode 8:01 
    Unimodal 8:11 
    Bimodal 8:19 
    Multimodal 8:24 
   Example: Mode 8:34 
   Example: Mode 9:53 
   Effect of Changing Units 10:31 
   Extra Example 1 1:53 
   Extra Example 2 1:36 
  Measuring Spread: Range, IQR, Standard Deviation 18:04
   Intro 0:00 
   Measuring Spread 0:08 
   Range 1:06 
    Example 1:16 
    Example 1:35 
   Standard Deviation 2:05 
    Population Standard Deviation 2:14 
    Sample Standard Deviation 3:13 
   Example: Standard Deviation 4:11 
   Example: Standard Deviation 6:05 
   Interquartile Range (IQR) 8:05 
   Example: Interquartile Range 9:03 
   Example: Interquartile Range 10:27 
   Extra Example 1 3:15 
   Extra Example 2 2:28 
  Measuring Position: Quartiles, Percentiles, Standardized Scores 16:28
   Intro 0:00 
   Measure of Position 0:09 
    Quartile, Percentile, Z-Scores 0:24 
   Quartiles (Q1, Q2, Q3) 0:32 
    Example 0:51 
   Example: Quartiles 1:28 
   Example: Quartiles 3:27 
   Percentiles 5:44 
   Example: Percentiles 6:19 
   Example: Percentiles 7:24 
   Standardized Score (Z-Score) 8:27 
   Example: Standardized Score 9:23 
   Example: Standardized Score 10:21 
   Extra Example 1 2:56 
   Extra Example 2 2:11 
  Boxplots 15:37
   Intro 0:00 
   What is a Boxplot? 0:05 
    Five Number Summary 0:15 
   Example: Boxplot 0:30 
   Example: Boxplot 4:33 
   Extra Example 1 3:09 
   Extra Example 2 2:21 
  Comparing Distributions of Univariate Data 24:19
   Intro 0:00 
   Comparing Features 0:07 
    Compare Center & Spread 0:11 
    Compare Clusters & Gaps 0:23 
    Compare Outliers and Unusual Features 0:33 
    Compare Shapes 0:55 
    Symmetric 1:00 
    Skewed Right 1:20 
    Skewed Left 1:31 
    Uniform 1:41 
   Example: Dotplots 1:56 
   Example: Back to Back Stemplots 5:16 
   Example: Parallel Boxplots 10:21 
   Example: Back to Back Stemplots 15:03 
   Extra Example 1 2:00 
   Extra Example 2 5:06 
  Exploring Bivariate Data: Scatterplots 13:45
   Intro 0:00 
   Bivariate Data 0:08 
    Example: Student Scores 0:31 
   Example: Scatterplot 1:08 
   Example: Scatterplot 2:36 
   Correlation and Linearity 3:49 
   Example: Correlation 5:30 
   Example: Correlation 6:55 
   Extra Example 1 3:10 
   Extra Example 2 2:21 
  Least Squares Regression 17:32
   Intro 0:00 
   Least Squares Regression Line 0:06 
    Why Least Squares? 0:25 
    Equations 1:21 
   Example 1: Age and Price 2:02 
   Example 2: Weld Diameter 5:47 
   Diagnostics 8:39 
    Residuals 8:58 
    Normal Probability Plot 10:09 
    Studentized Residuals (Hat Matrix) 10:29 
   Transformations 10:48 
    Logarithmic Transformation 11:04 
    Square Root Transformation 11:44 
   Extra Example 1 3:07 
   Extra Example 2 2:11 
  Exploring Categorical Data 17:00
   Intro 0:00 
   Frequency Tables 0:05 
    Example: Student Age 0:16 
    Relative Frequency 0:55 
   Bar Graphs 1:59 
   Marginal and Joint Probabilities 3:54 
   Example 1: Gender and Beer 6:52 
   Conditional Probabilities 8:47 
   Example 2: Gender and Beer 11:41 
   Extra Example 1 2:09 
   Extra Example 2 1:56 
III. Sampling and Experimentation
  Methods of Data Collection 12:04
   Intro 0:00 
   Purpose 0:05 
   Census 1:22 
    Example: US Census 1:36 
    Example: Fireworks Factory 2:34 
   Sample Survey 3:41 
   Experiment 6:12 
    Example: Coke vs Pepsi 7:09 
   Observational Study 8:19 
   Observational or Experiment 9:30 
    Example 1 9:53 
    Example 2 10:24 
    Example 3 11:17 
  Planning and Conducting Surveys 13:51
   Intro 0:00 
   Ideal Surveys 0:06 
    Random Selection 0:16 
   Characteristics of Surveys 0:42 
    Chance 0:50 
    Random Samples 1:02 
    No Source of Bias 1:32 
   Populations, Samples, Random Selection 2:21 
    Population 2:28 
    Sample 2:51 
   Sources of Bias 4:14 
    Example 4:33 
   Sampling Methods 7:27 
    Simple Random Sampling (SRS) 7:40 
    Example 8:33 
    Stratified Random Sampling (Strata) 10:03 
    Example 11:06 
    Cluster Sampling 12:19 
    Example 13:06 
  Planning and Conducting Experiments 19:32
   Intro 0:00 
   Purpose 0:06 
   Characteristics 1:00 
   Basic Terms 2:00 
    Treatment 2:12 
    Control Group 2:30 
    Experimental Units 3:17 
    Random Assignment 3:38 
    Replication 4:19 
   Sources of Bias and Confounding 4:48 
    Counfounding 5:00 
    Example 5:29 
    Placebo Effect 6:41 
    Example 7:08 
    Blinding 7:56 
    Example 8:24 
   Completely Randomized Design 9:12 
   Randomized Block Design 12:44 
    Block 12:55 
    Matched Pairs 13:22 
    Example 13:41 
    Randomized Block Design 15:09 
    Example 15:30 
   Studies and Surveys vs Experiments 17:03 
IV. Probability
  Experiment, Outcomes, and Sample Space 14:54
   Intro 0:00 
   Basic Definitions 0:29 
    Experiment 0:35 
    Outcomes 0:55 
    Sample Space 1:04 
   Examples 1:34 
    Roll a Die 1:39 
    Flip a Coin 2:33 
   Simple and Compound Events 3:30 
    Event 3:43 
    Simple Event 3:58 
    Compound Event 4:27 
   Example 5:14 
   Extra Example 1 0:59 
   Extra Example 2 4:21 
  Calculating Probability 14:13
   Intro 0:00 
   What is Probability 0:27 
    Range 0:53 
    Sum of Probabilities 1:26 
    Example: Football Game 2:05 
   Classical Probability 2:53 
    Equally Likely Outcomes 3:05 
    Example: Coin Flip 4:08 
    Example: Die Roll 5:12 
   Relative Frequency 6:44 
    Example 7:22 
   Subjective Probability 9:38 
    Example 10:06 
   Extra Example 1 1:04 
   Extra Example 2 1:33 
  Probability and Events 22:08
   Intro 0:00 
   Mutually Exclusive Events 0:17 
    Example: Coin Flip 0:27 
    Example: Die Roll 3:03 
   Independent Events 5:13 
    Notation 3:31 
    Example: Coin 6:01 
   Independent Events, cont. 9:19 
    Example: Coffee Drinkers 9:23 
   Mutually Exclusive vs Independent 13:03 
   Complementary Events 14:08 
    Example: Coffee Drinkers 15:37 
   Extra Example 1 1:16 
   Extra Example 2 3:32 
  Intersection of Events and the Multiplication Rule 19:58
   Intro 0:00 
   Intersection of Events 0:08 
    Venn Diagram 1:20 
   Multiplication Rule 2:22 
    Joint Probability 2:23 
    Example 3:23 
   Example 6:30 
   Multiplication Rule for Independent Events 10:30 
    Example 11:39 
   Joint Probability of Mutually Exclusive Events 15:24 
   Extra Example 1 1:24 
   Extra Example 2 2:09 
  Union of Events and the Addition Rule 18:28
   Intro 0:00 
   Union of Events 0:06 
    Venn Diagram 0:52 
   Addition Rule 2:01 
    Example: Coffee Drinkers 3:25 
   Example 6:26 
   Addition Rule for Mutually Exclusive Events 9:11 
   Example 10:27 
   Extra Example 1 2:41 
   Extra Example 2 1:15 
  Bayes' Rule 16:59
   Intro 0:00 
   Partition of Events 0:07 
    Venn Diagram 0:17 
   Law of Total Probability 3:12 
   Bayes' Rule 6:11 
   Example 9:09 
   Extra Example 1 4:07 
V. Discrete Random Variables and Probability Distribution
  Random Variables 7:52
   Intro 0:00 
   Definition 0:06 
    Example 0:24 
   Discrete Random Variables 1:22 
    Example 1:56 
   Continuous Random Variable 3:53 
    Example 4:12 
   Extra Example 1 1:51 
  Probability Distribution of a Discrete Random Variable 15:55
   Intro 0:00 
   Definition 0:09 
    Example 0:24 
   Rules of a Probability Distribution 3:27 
    Rule 1 3:33 
    Rule 2 4:30 
    Example 1 4:59 
    Example 2 6:00 
    Example 3 6:38 
   Example: Defective DVDs 7:19 
   Extra Example 1 1:56 
   Extra Example 2 1:28 
  Mean and Standard Deviation of a Discrete Random Variable 17:37
   Intro 0:00 
   Mean of a Discrete Random Variable 0:10 
    Example 1:17 
   Example: Numbers Game 3:09 
   Standard Deviation of a Discrete Random Variable 6:02 
    Example 7:46 
   Example: Cars in a Town 10:12 
   Extra Example 1 2:24 
   Extra Example 2 2:22 
  Factorials, Combinations, Permutations 15:43
   Intro 0:00 
   Counting Rule 0:08 
    Example: Coin Toss 0:56 
    Example: Football Team 1:45 
   Factorials 2:54 
    Example 3:39 
    Zero Factorial 4:03 
    Example 4:20 
   Combinations 5:16 
    Example 6:23 
   Permutations 8:16 
    Example 9:01 
   Extra Example 1 2:58 
   Extra Example 2 2:20 
  Binomial Probability Distribution 21:38
   Intro 0:00 
   Binomial Experiment 0:07 
    Discrete Random Variable 0:34 
    Trial 1:01 
    Bernoulli Trials 1:26 
   Example: Roll Die 2:37 
   Binomial Probability Distribution 4:36 
   Example: Winter Holiday Stress 6:58 
   Example: MRI 9:51 
   Probability of Success and Shape 12:42 
    Symmetric 12:54 
    Skewed Right 13:23 
    Skewed Left 14:13 
   Mean/Standard Deviation of Binomial Distribution 15:03 
    Example: Stress 16:06 
    Example: MRI 17:07 
   Extra Example 1 1:47 
   Extra Example 2 1:49 
  Poisson Probability Distribution 13:40
   Intro 0:00 
   Poisson Probability Distribution 0:06 
    Conditions 0:43 
   Example: Complaints 3:18 
   Example: Failed Businesses 5:01 
   Mean/Standard Deviation of Poisson Distribution 7:52 
    Example: Complaints 8:53 
    Example: Failed Businesses 9:46 
   Extra Example 1 1:19 
   Extra Example 2 1:48 
  Geometric and Hypergeometric Probability Distributions 19:08
   Intro 0:00 
   Geometric Probability Distribution 0:08 
   Example: Engine Malfunction 3:00 
   Example: Interviews 5:45 
   Hypergeometric Probability Distribution 7:36 
   Example: Engineers 10:16 
   Example: Marbles 12:55 
   Extra Example 1 1:14 
   Extra Example 2 2:00 
  Combining Independent Random Variables 20:26
   Intro 0:00 
   Independence vs Dependence 0:09 
   Mean of Sums for Independent Random Variables 2:32 
   Example 4:02 
   Example 5:58 
   Variance for Sums of Independent Random Variables 8:49 
   Example 10:30 
   Example 12:26 
   Extra Example 1 3:04 
   Extra Example 2 1:59 
VI. Continuous Random Variables and the Normal Distribution
  Continuous Probability Distribution 6:19
   Intro 0:00 
   Continuous Random Variable 0:07 
   Probability Density Function 0:54 
   More About Densities 3:07 
   More About Densities, cont. 4:06 
  Normal Distribution 6:42
   Intro 0:00 
   Normal Distribution 0:05 
    Bell Shaped Curve 0:09 
   Properties of the Normal Distribution 1:02 
    Area Under the Curve (Density Curve) 1:07 
   Symmetric About the Mean 1:40 
   Two Tails 2:21 
   Normal Distribution 3:07 
    Different Means 3:10 
   Different Standard Deviations 4:32 
  Standard Normal Distribution 13:25
   Intro 0:00 
   Standard Normal Distribution 0:06 
    Z-Scores 1:08 
   Examples 1:57 
   More Examples 4:43 
   More Examples 7:12 
   Extra Example 1 1:51 
   Extra Example 2 1:33 
  Standardizing a Normal Distribution 12:22
   Intro 0:00 
   Standardizing a Normal Distribution 0:07 
    Mean and Standard Deviation of X 1:13 
   Examples 1:39 
   More Examples 3:22 
   More Examples 6:17 
   Extra Example 1 1:55 
   Extra Example 2 1:12 
  Applications of the Normal Distribution 12:20
   Intro 0:00 
   Standardizing a Normal Distribution 0:08 
   Example: US Debt 0:59 
   Example: Toy Assembly 3:19 
   Example: Soda 5:04 
   Example: Calculator 7:27 
   Extra Example 1 1:31 
   Extra Example 2 1:45 
  Finding Values When the Probability is Known 12:44
   Intro 0:00 
   Example 1 0:10 
   Example 2 1:32 
   Example 3 3:12 
   Example 4: Battery Life 4:18 
   Example 5: SAT Scores 6:33 
   Extra Example 1 1:24 
   Extra Example 2 2:21 
VII. Sampling Distributions
  Population and Sampling Distributions 12:02
   Intro 0:00 
   Population Distribution 0:06 
    Example: Teaching Experience 0:14 
   Sampling Distribution 1:31 
   Example: Teaching Experience 2:16 
   Sampling Error 5:29 
    Random and No Non-Sampling Error 6:00 
    Example 6:10 
   Non-Sampling Error 7:22 
    Example 7:38 
   Example: Six Numbers 9:17 
  Mean, Standard Deviation, and the Shape of the Sampling Distribution of the Sampling Mean 4:57
   Intro 0:00 
   Mean/Standard Deviation of Sample Mean 0:10 
    Estimator 0:57 
    Unbiased Estimator 1:15 
   Sampling Distribution of Sample Mean 1:50 
    Spread 1:53 
    Standard Deviation 2:18 
    Consistent Estimator 2:40 
   Shape of Sampling Distribution 2:51 
    Normal 3:21 
   Shape of Sampling Distribution, cont. 3:50 
    Central Limit Theorem 4:15 
  Applications of the Sampling Distribution of the Sample Mean 14:50
   Intro 0:00 
   Example 1: Speed Limit 0:08 
   Example 2: Speed Limit 2:50 
   Example 3: Speed Limit 4:20 
   Example 4: Study Times 6:20 
   Example 5: Study Times 9:02 
   Extra Example 1 2:14 
   Extra Example 2 2:12 
  Mean, Standard Deviation, and the Shape of the Sampling Distribution of the Sample Proportion 3:58
   Intro 0:00 
   Population vs Sample Proportions 0:10 
    Population Proportion 0:16 
    Sample Proportion 0:23 
    Sample: Eye Color 0:36 
   Mean/Standard Deviation of Sample Proportion 1:47 
    Mean 1:51 
    Unbiased Estimator 2:07 
    Standard Deviation 2:28 
   Shape of the Distribution 3:07 
  Applications of the Sampling Distribution of the Sample Proportion 10:45
   Intro 0:00 
   Example 1: Retirement Plan 0:07 
   Example 2: Retirement Plan 3:04 
   Example 3: Voters 4:35 
   Extra Example 1 2:27 
   Extra Example 2 1:40 
VIII. Estimation of the Mean and Proportion
  Introduction to Estimation 12:52
   Intro 0:00 
   Estimation 0:06 
    Parameter 0:29 
    Estimate 1:02 
    Estimator 1:10 
    Example 1:20 
   Steps for Estimation 2:21 
    Example: Dartboard 3:08 
    Consistent/Bias 3:41 
    Inconsistent/Unbiased 4:09 
    Consistent/Unbiased 4:44 
   Point Estimate 5:33 
    Example 5:50 
   Interval Estimate 6:35 
    Margin of Error 7:15 
   Confidence Interval 7:35 
    Confidence Level 7:55 
   Example 8:10 
   More on Confidence Intervals 10:18 
    Confidence Level Increase 11:41 
    Sample Size Increase 12:25 
  Estimation of a Population Mean: Standard Deviation Known 17:03
   Intro 0:00 
   Population is Normal, n<30 0:10 
    Confidence Interval 0:28 
   Example 1 2:34 
   Example 2 5:54 
   When n>30, Any Distribution 7:58 
    Confidence Interval 8:48 
   Example 3 9:14 
   Example 4 11:16 
   Extra Example 1 2:24 
   Extra Example 2 1:34 
  Sample Size for Estimation of a Population Mean 10:39
   Intro 0:00 
   Determining Sample Size 0:07 
    Finding n 0:30 
    Origin of Equation 0:56 
   Example 1 2:16 
   Example 2 4:42 
   Extra Example 1 2:13 
   Extra Example 2 1:43 
  Estimation of Population Mean: Sigma Not Known 19:25
   Intro 0:00 
   t-Distribution 0:10 
   Examples: t-Distribution 0:38 
   Using the t-Distribution 4:25 
    Confidence Interval 5:03 
   Example 1: Waiting Time 5:54 
   Example 2: MPG 9:35 
   Extra Example 1 3:23 
   Extra Example 2 2:54 
  Estimation of Population Proportion: Large Sample 17:26
   Intro 0:00 
   Population vs Sample Proportion 0:10 
   Confidence Intervals for p 1:50 
   Example 1: Credit 2:18 
   Example 2: Time 4:59 
   Sample Size for the Estimation of p 7:31 
    Margin of Error 7:55 
    Conservative Estimate 8:17 
   Example 3: Gambling 8:40 
   Example 4: Clocks 10:53 
   Extra Example 1 2:32 
   Extra Example 2 1:50 
  Large Sample Confidence Intervals for Difference in Population Proportion 16:16
   Intro 0:00 
   Sampling Distribution for Difference in Sample Proportion 0:08 
    Large and Independent Samples 0:11 
    Confidence Intervals for p1-p2 1:28 
   Example 1: Toothpaste 2:04 
   Example 2: Seat Belts 6:20 
   Extra Example 1 3:32 
   Extra Example 2 2:50 
  Confidence Intervals for a Difference in Means 27:58
   Intro 0:00 
   Independent Samples: Standard Deviations Known 0:07 
   Confidence Interval for Difference of Means 1:12 
   Example 1: Starting Salary 1:35 
   Example 2: Fill 5:36 
   Independent Samples: Standard Deviations Not Known 7:54 
   Pooled Standard Deviation for Two Samples 8:46 
   Confidence Interval for Difference of Means 9:32 
   Example 3: Caffeine 10:35 
   Example 4: Test Scores 15:20 
   Inference about Difference of Means for Paired Samples 19:05 
    Paired or Matched Sample 19:21 
   Inference about Difference of Means for Paired Samples 20:58 
   Extra Example 1 3:40 
   Extra Example 2 2:03 
  Confidence Intervals for the Slope of a Least Squares Regression Line 18:47
   Intro 0:00 
   Sampling Distribution of b 0:08 
   Calculating the Estimator of Standard Deviation of b 1:03 
   Confidence Interval for Beta 1:31 
   Example 1: Age and Price 2:24 
   Example 2: Weld Diameter 6:41 
   Extra Example 1 4:27 
   Extra Example 2 3:37 
IX. Tests of Significance
  Introduction: Hypothesis Tests 14:09
   Intro 0:00 
   Two Hypotheses 0:13 
    Null Hypothesis 0:21 
    Alternative Hypothesis 0:36 
    Example 1:05 
   Example: Two Hypotheses 1:43 
   Rejection and Non-Rejection Regions 3:25 
   Type 1 and Type 2 Errors 5:30 
    Type 1 Error 6:44 
    Significance Level 7:08 
    Type 2 Error 7:42 
    Power of the Test 8:30 
   Tails of the Test 9:29 
  Large Sample Test for a Proportion 14:30
   Intro 0:00 
   Test Statistic Z 0:08 
    Why Z? 0:29 
   Example 1: TV Violence 1:10 
   Example 2: Smoking 5:16 
   Extra Example 1 3:25 
   Extra Example 2 2:52 
  Large Sample Test for a Difference in Two Proportions 19:14
   Intro 0:00 
   Pooled Estimate of P1 and P2 0:09 
   Example 1: Softball Bases 1:34 
   Example 2: Sleep Problems 6:59 
   Extra Example 1 4:11 
   Extra Example 2 4:12 
  Test for a Mean 14:57
   Intro 0:00 
   Standard Deviation is Known 0:07 
    Central Limit Theory for n>30 0:32 
   Example 1: Cheese Weight 0:53 
   Example 2: Observations 3:53 
   Standard Deviation Not Known 6:15 
    t-Distribution Usage 6:24 
    Degrees of Freedom 6:53 
   Example 3: Height 7:01 
   Example 4: Sampling 9:50 
   Extra Example 1 2:02 
   Extra Example 2 1:32 
  Test for a Difference Between Two Means 23:13
   Intro 0:00 
   Standard Deviation Known, Unpaired 0:08 
   Example 1: Boredom 1:17 
   Example 2: Smoking 4:15 
   Population Standard Deviations Unknown, But Equal 7:10 
    Pooled Standard Deviation for Two Samples 7:49 
   Example 3: Diet Soda 8:28 
   Example 4: TV 12:12 
   Paired Samples 15:50 
   Example 5: Hormone Level 16:33 
   Example 6: Hypnotism 19:43 
  Chi-Square Tests: One Way and Two Way 24:33
   Intro 0:00 
   Goodness of Fit Test 0:07 
    Right-Tailed Test 0:52 
   Example 1: Die Rolls 1:16 
   Example 2: Stolen Vehicles 3:31 
   Test of Independence 7:02 
   Example 3: Debt 7:51 
   Example 4: Contraceptive Use 13:14 
   Test of Homogeneity 16:31 
   Example 5: New Product 17:09 
   Example 6: Oil 21:24 
  Hypothesis Testing for the Slope of a Least Squares Regression Line 17:48
   Intro 0:00 
   Sampling Distribution of b 0:08 
   Calculating the Estimator of Standard Deviation of b 1:18 
   Hypothesis Testing for Beta 1:50 
   Example 1: Age 2:25 
   Example 2: Weld Diameter 6:42 
   Extra Example 1 3:30 
   Extra Example 2 3:10