Section 1: 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 
6:59  
 
 Percentiles 
7:10  
 
 Quartiles 
7:39  
 
Example: Find the Quartiles 
8:58  
 
Measuring Spread 
11:13  
 
 Range 
11:14  
 
 IQR 
11:33  
 
 Variance 
11:55  
 
 Example: Measuring Spread 
13:21  
 
Example: Find the Measures of Spread 
22:09  
 
Outliers 
27:23  
 
 Outliers 
27:24  
 
Example: Outliers 
29:05  
 
Boxplots 
31:44  
 
 5number Summary 
31:45  
 
Example I: Boxplot 
33:55  
 
Describe the Distribution 
44:20  
 
 SOCS: Shape, Outlier, Center, Spread 
44:21  
 
 Choosing Your Measure of Center & Spread 
45:16  
 
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  
Section 2: 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  
 
 yintercept, Slope, Mean, and SD 
10:46  
 
Example: Interpreting the LSRL 
14:48  
 
 Step 1: Determine the Leastsquares Regression Line 
14:49  
 
 Step 2: Interpret the Slope and yintercept of the Regression Line 
18:28  
 
 Step 3: Interpret the Correlation 
20:56  
 
Coefficient of Determination 
23:50  
 
 R² = (r)² 
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 Leastsquares 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  
Section 3: 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 WellDesigned and WellConducted 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  
 
NonRandom 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 Welldesigned and Wellconducted 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  
 
Doubleblind Experiment 
31:06  
 
 Doubleblind Experiment 
31:07  
 
Example VIII: Doubleblind Experiment 
32:37  
 
Example IX: Design a Study to Test Hypothesis 
37:04  
 
Generalizability of Results 
40:39  
 
 Statistically Significant Data 
40:40  
Section 4: 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  
 
Example III: Advertising Contracts 
12:23  
 
 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  
 
ZScore 
8:17  
 
 ZScore 
8:18  
 
Example II: ZScore 
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 Zscore to Calculate Probability 
16:01  
 
 Step 1: Sketch 
16:02  
 
 Step 2: Calculate Zscore 
18:16  
 
 Step 3: Solve for Probability Using the Normal Table 
19:14  
 
Example IV: Using the Normal Table and Zscore to Calculate Probability 
20:29  
 
 Step 1: Sketch 
20:30  
 
 Step 2: Calculate Zscore 
21:52  
 
 Step 3: Solve for Probability Using the Normal Table 
22:36  
 
Example V: Using the Normal Table and Zscore to Calculate Probability 
27:20  
 
 Step 1: Sketch 
27:42  
 
 Step 2: Calculate Zscore 
28:14  
 
 Step 3: Solve for Probability Using the Normal Table 
29:45  
 
Example VI: Using the Normal Table and Zscore to Calculate Probability 
34:00  
 
 Step 1: Sketch 
34:01  
 
 Step 2: Calculate Zscore 
35:48  
 
 Step 3: Solve for Probability Using the Normal Table 
36:56  
 
Example VII: Using the Normal Table and Zscore to Calculate Probability 
41:21  
 
 Step 1: Sketch 
41:22  
 
 Step 2: Calculate Zscore 
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 Zscore 
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 Zscore 
56:36  
 
 Step 3: Solve for Mean 
57:42  
Section 6: 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  
Section 7: 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  
 
ZTable 
6:06  
 
 ZTable 
6:07  
 
TTable 
7:07  
 
 TTable 
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  
 
 Calculating Using Your Calculator 
10:46  
 
Steps to Calculating a Confidence Interval 
12:09  
 
 Step 1: Read 
12:10  
 
 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 Pvalue & 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 Pvalue 
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 Pvalue 
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 Pvalue 
24:39  
 
 5. Interpret 
27:23  
 
Matched Pairs Ttest 
29:34  
 
 Matched Pairs Ttest 
29:35  
 
 1. Write the Hypothesis 
33:05  
 
 2. Check Conditions 
34:58  
 
 3. Calculate the Test Statistic 
35:52  
 
 4. Look Up the Pvalue 
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  
 
Writing Your Hypothesis 
3:36  
 
 Writing Your Hypothesis 
3:37  
 
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 Pvalue 
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 Pvalue 
36:27  
 
 5. Interpret 
39:01  
 
Example I: Two Samples Hypothesis Testing 
42:11  
 
Example II: Two Samples Hypothesis Testing 
53:30  
 
“Pick Your Test” Map 
70:47  
 
 “Pick Your Test” Map 
70:48  
 
Example III: Reliability Testing 
78:31  

Hypothesis Testing of LeastSquares Regression Line 
53:49 
 
Intro 
0:00  
 
Lesson Overview 
0:10  
 
Review of Leastsquares Regression and Interpretation 
0:29  
 
 Correlation Coefficient ( r ) 
0:30  
 
 Equation of the Leastsquares Regression Line 
1:02  
 
Example 
2:45  
 
 Part A: Leastsquares Regression Line 
2:46  
 
 Part B: Slope of the Leastsquares Regression Line 
6:03  
 
Test for the Regression Line 
7:50  
 
 Is There a Correlation? 
7:51  
 
 Is the yintercept = 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: Leastsquares Regression Equation 
38:47  
 
 Part B: Standard Error 
40:01  
 
 Part C: Slope of the Leastsquares Regression Line 
41:21  
 
 Part D: Null and Alternative Hypotheses 
42:08  
 
 Part E: Value of Test Statistic 
43:09  
 
 Part G: PValue 
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 (ChiSquared Tests) 
1:12:55 
 
Intro 
0:00  
 
Lesson Overview 
0:11  
 
How Do We Know to Use a ChiSquared Test? 
0:27  
 
 Categorical Data 
0:28  
 
ChiSquared 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: ChiSquared Goodness of Fit Test 
4:02  
 
 ChiSquared 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: PValue & ChiSquare Distribution Table 
17:03  
 
Example II: ChiSquared Goodness of Fit Test 
22:04  
 
 Step 1: Hypothesis 
22:05  
 
 Step 2: Expected 
24:55  
 
 Step 3: Calculate 
29:05  
 
 Step 4: PValue & ChiSquare Distribution Table 
33:18  
 
ChiSquared Test of: Homogeneity or Independence/Association 
34:31  
 
 Homogeneity 
34:32  
 
 Independence/Association 
35:42  
 
Example III: ChiSquared 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: PValue & ChiSquare Distribution Table 
49:30  
 
As a Test of Association 
52:53  
 
 As a Test of Association 
52:54  
 
Example IV: ChiSquared Test of: Homogeneity or Independence/Association 
55:05  
 
 Step 1: Hypothesis, Expected, and Conditions 
55:06  
 
 Step 2: Calculate 
59:45  
 
 Step3: PValue & ChiSquare Distribution Table 
61:51  
 
Example V: ChiSquared 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: PValue & ChiSquare Distribution Table 
70:50  
Section 8: 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  