I. Introduction 

Descriptive Statistics vs. Inferential Statistics 
25:31 
 
Intro 
0:00  
 
Roadmap 
0:10  
 
 Roadmap 
0:11  
 
Statistics 
0:35  
 
 Statistics 
0:36  
 
Let's Think About High School Science 
1:12  
 
 Measurement and Find Patterns (Mathematical Formula) 
1:13  
 
Statistics = Math of Distributions 
4:58  
 
 Distributions 
4:59  
 
 Problematic… but also GREAT 
5:58  
 
Statistics 
7:33  
 
 How is It Different from Other Specializations in Mathematics? 
7:34  
 
 Statistics is Fundamental in Natural and Social Sciences 
7:53  
 
Two Skills of Statistics 
8:20  
 
 Description (Exploration) 
8:21  
 
 Inference 
9:13  
 
Descriptive Statistics vs. Inferential Statistics: Apply to Distributions 
9:58  
 
 Descriptive Statistics 
9:59  
 
 Inferential Statistics 
11:05  
 
Populations vs. Samples 
12:19  
 
 Populations vs. Samples: Is it the Truth? 
12:20  
 
 Populations vs. Samples: Pros & Cons 
13:36  
 
 Populations vs. Samples: Descriptive Values 
16:12  
 
Putting Together Descriptive/Inferential Stats & Populations/Samples 
17:10  
 
 Putting Together Descriptive/Inferential Stats & Populations/Samples 
17:11  
 
Example 1: Descriptive Statistics vs. Inferential Statistics 
19:09  
 
Example 2: Descriptive Statistics vs. Inferential Statistics 
20:47  
 
Example 3: Sample, Parameter, Population, and Statistic 
21:40  
 
Example 4: Sample, Parameter, Population, and Statistic 
23:28  
II. About Samples: Cases, Variables, Measurements 

About Samples: Cases, Variables, Measurements 
32:14 
 
Intro 
0:00  
 
Data 
0:09  
 
 Data, Cases, Variables, and Values 
0:10  
 
 Rows, Columns, and Cells 
2:03  
 
 Example: Aircrafts 
3:52  
 
How Do We Get Data? 
5:38  
 
 Research: Question and Hypothesis 
5:39  
 
 Research Design 
7:11  
 
 Measurement 
7:29  
 
 Research Analysis 
8:33  
 
 Research Conclusion 
9:30  
 
Types of Variables 
10:03  
 
 Discrete Variables 
10:04  
 
 Continuous Variables 
12:07  
 
Types of Measurements 
14:17  
 
 Types of Measurements 
14:18  
 
Types of Measurements (Scales) 
17:22  
 
 Nominal 
17:23  
 
 Ordinal 
19:11  
 
 Interval 
21:33  
 
 Ratio 
24:24  
 
Example 1: Cases, Variables, Measurements 
25:20  
 
Example 2: Which Scale of Measurement is Used? 
26:55  
 
Example 3: What Kind of a Scale of Measurement is This? 
27:26  
 
Example 4: Discrete vs. Continuous Variables. 
30:31  
III. Visualizing Distributions 

Introduction to Excel 
8:09 
 
Intro 
0:00  
 
Before Visualizing Distribution 
0:10  
 
 Excel 
0:11  
 
Excel: Organization 
0:45  
 
 Workbook 
0:46  
 
 Column x Rows 
1:50  
 
 Tools: Menu Bar, Standard Toolbar, and Formula Bar 
3:00  
 
Excel + Data 
6:07  
 
 Exce and Data 
6:08  

Frequency Distributions in Excel 
39:10 
 
Intro 
0:00  
 
Roadmap 
0:08  
 
 Data in Excel and Frequency Distributions 
0:09  
 
Raw Data to Frequency Tables 
0:42  
 
 Raw Data to Frequency Tables 
0:43  
 
 Frequency Tables: Using Formulas and Pivot Tables 
1:28  
 
Example 1: Number of Births 
7:17  
 
Example 2: Age Distribution 
20:41  
 
Example 3: Height Distribution 
27:45  
 
Example 4: Height Distribution of Males 
32:19  

Frequency Distributions and Features 
25:29 
 
Intro 
0:00  
 
Roadmap 
0:10  
 
 Data in Excel, Frequency Distributions, and Features of Frequency Distributions 
0:11  
 
Example #1 
1:35  
 
 Uniform 
1:36  
 
Example #2 
2:58  
 
 Unimodal, Skewed Right, and Asymmetric 
2:59  
 
Example #3 
6:29  
 
 Bimodal 
6:30  
 
Example #4a 
8:29  
 
 Symmetric, Unimodal, and Normal 
8:30  
 
 Point of Inflection and Standard Deviation 
11:13  
 
Example #4b 
12:43  
 
 Normal Distribution 
12:44  
 
Summary 
13:56  
 
 Uniform, Skewed, Bimodal, and Normal 
13:57  
 
Sketch Problem 1: Driver's License 
17:34  
 
Sketch Problem 2: Life Expectancy 
20:01  
 
Sketch Problem 3: Telephone Numbers 
22:01  
 
Sketch Problem 4: Length of Time Used to Complete a Final Exam 
23:43  

Dotplots and Histograms in Excel 
42:42 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
Previously 
1:02  
 
 Data, Frequency Table, and visualization 
1:03  
 
Dotplots 
1:22  
 
 Dotplots Excel Example 
1:23  
 
Dotplots: Pros and Cons 
7:22  
 
 Pros and Cons of Dotplots 
7:23  
 
 Dotplots Excel Example Cont. 
9:07  
 
Histograms 
12:47  
 
 Histograms Overview 
12:48  
 
 Example of Histograms 
15:29  
 
Histograms: Pros and Cons 
31:39  
 
 Pros 
31:40  
 
 Cons 
32:31  
 
Frequency vs. Relative Frequency 
32:53  
 
 Frequency 
32:54  
 
 Relative Frequency 
33:36  
 
Example 1: Dotplots vs. Histograms 
34:36  
 
Example 2: Age of Pennies Dotplot 
36:21  
 
Example 3: Histogram of Mammal Speeds 
38:27  
 
Example 4: Histogram of Life Expectancy 
40:30  

Stemplots 
12:23 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
What Sets Stemplots Apart? 
0:46  
 
 Data Sets, Dotplots, Histograms, and Stemplots 
0:47  
 
Example 1: What Do Stemplots Look Like? 
1:58  
 
Example 2: BacktoBack Stemplots 
5:00  
 
Example 3: Quiz Grade Stemplot 
7:46  
 
Example 4: Quiz Grade & Afterschool Tutoring Stemplot 
9:56  

Bar Graphs 
22:49 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:08  
 
Review of Frequency Distributions 
0:44  
 
 Yaxis and Xaxis 
0:45  
 
 Types of Frequency Visualizations Covered so Far 
2:16  
 
 Introduction to Bar Graphs 
4:07  
 
Example 1: Bar Graph 
5:32  
 
 Example 1: Bar Graph 
5:33  
 
Do Shapes, Center, and Spread of Distributions Apply to Bar Graphs? 
11:07  
 
 Do Shapes, Center, and Spread of Distributions Apply to Bar Graphs? 
11:08  
 
Example 2: Create a Frequency Visualization for Gender 
14:02  
 
Example 3: Cases, Variables, and Frequency Visualization 
16:34  
 
Example 4: What Kind of Graphs are Shown Below? 
19:29  
IV. Summarizing Distributions 

Central Tendency: Mean, Median, Mode 
38:50 
 
Intro 
0:00  
 
Roadmap 
0:07  
 
 Roadmap 
0:08  
 
Central Tendency 1 
0:56  
 
 Way to Summarize a Distribution of Scores 
0:57  
 
 Mode 
1:32  
 
 Median 
2:02  
 
 Mean 
2:36  
 
Central Tendency 2 
3:47  
 
 Mode 
3:48  
 
 Median 
4:20  
 
 Mean 
5:25  
 
Summation Symbol 
6:11  
 
 Summation Symbol 
6:12  
 
Population vs. Sample 
10:46  
 
 Population vs. Sample 
10:47  
 
Excel Examples 
15:08  
 
 Finding Mode, Median, and Mean in Excel 
15:09  
 
Median vs. Mean 
21:45  
 
 Effect of Outliers 
21:46  
 
 Relationship Between Parameter and Statistic 
22:44  
 
 Type of Measurements 
24:00  
 
 Which Distributions to Use With 
24:55  
 
Example 1: Mean 
25:30  
 
Example 2: Using Summation Symbol 
29:50  
 
Example 3: Average Calorie Count 
32:50  
 
Example 4: Creating an Example Set 
35:46  

Variability 
42:40 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Variability (or Spread) 
0:45  
 
 Variability (or Spread) 
0:46  
 
Things to Think About 
5:45  
 
 Things to Think About 
5:46  
 
Range, Quartiles and Interquartile Range 
6:37  
 
 Range 
6:38  
 
 Interquartile Range 
8:42  
 
Interquartile Range Example 
10:58  
 
 Interquartile Range Example 
10:59  
 
Variance and Standard Deviation 
12:27  
 
 Deviations 
12:28  
 
 Sum of Squares 
14:35  
 
 Variance 
16:55  
 
 Standard Deviation 
17:44  
 
Sum of Squares (SS) 
18:34  
 
 Sum of Squares (SS) 
18:35  
 
Population vs. Sample SD 
22:00  
 
 Population vs. Sample SD 
22:01  
 
Population vs. Sample 
23:20  
 
 Mean 
23:21  
 
 SD 
23:51  
 
Example 1: Find the Mean and Standard Deviation of the Variable Friends in the Excel File 
27:21  
 
Example 2: Find the Mean and Standard Deviation of the Tagged Photos in the Excel File 
35:25  
 
Example 3: Sum of Squares 
38:58  
 
Example 4: Standard Deviation 
41:48  

Five Number Summary & Boxplots 
57:15 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
Summarizing Distributions 
0:37  
 
 Shape, Center, and Spread 
0:38  
 
 5 Number Summary 
1:14  
 
Boxplot: Visualizing 5 Number Summary 
3:37  
 
 Boxplot: Visualizing 5 Number Summary 
3:38  
 
Boxplots on Excel 
9:01  
 
 Using 'Stocks' and Using Stacked Columns 
9:02  
 
 Boxplots on Excel Example 
10:14  
 
When are Boxplots Useful? 
32:14  
 
 Pros 
32:15  
 
 Cons 
32:59  
 
How to Determine Outlier Status 
33:24  
 
 Rule of Thumb: Upper Limit 
33:25  
 
 Rule of Thumb: Lower Limit 
34:16  
 
 Signal Outliers in an Excel Data File Using Conditional Formatting 
34:52  
 
Modified Boxplot 
48:38  
 
 Modified Boxplot 
48:39  
 
Example 1: Percentage Values & Lower and Upper Whisker 
49:10  
 
Example 2: Boxplot 
50:10  
 
Example 3: Estimating IQR From Boxplot 
53:46  
 
Example 4: Boxplot and Missing Whisker 
54:35  

Shape: Calculating Skewness & Kurtosis 
41:51 
 
Intro 
0:00  
 
Roadmap 
0:16  
 
 Roadmap 
0:17  
 
Skewness Concept 
1:09  
 
 Skewness Concept 
1:10  
 
Calculating Skewness 
3:26  
 
 Calculating Skewness 
3:27  
 
Interpreting Skewness 
7:36  
 
 Interpreting Skewness 
7:37  
 
 Excel Example 
8:49  
 
Kurtosis Concept 
20:29  
 
 Kurtosis Concept 
20:30  
 
Calculating Kurtosis 
24:17  
 
 Calculating Kurtosis 
24:18  
 
Interpreting Kurtosis 
29:01  
 
 Leptokurtic 
29:35  
 
 Mesokurtic 
30:10  
 
 Platykurtic 
31:06  
 
 Excel Example 
32:04  
 
Example 1: Shape of Distribution 
38:28  
 
Example 2: Shape of Distribution 
39:29  
 
Example 3: Shape of Distribution 
40:14  
 
Example 4: Kurtosis 
41:10  

Normal Distribution 
34:33 
 
Intro 
0:00  
 
Roadmap 
0:13  
 
 Roadmap 
0:14  
 
What is a Normal Distribution 
0:44  
 
 The Normal Distribution As a Theoretical Model 
0:45  
 
Possible Range of Probabilities 
3:05  
 
 Possible Range of Probabilities 
3:06  
 
What is a Normal Distribution 
5:07  
 
 Can Be Described By 
5:08  
 
 Properties 
5:49  
 
'Same' Shape: Illusion of Different Shape! 
7:35  
 
 'Same' Shape: Illusion of Different Shape! 
7:36  
 
Types of Problems 
13:45  
 
 Example: Distribution of SAT Scores 
13:46  
 
Shape Analogy 
19:48  
 
 Shape Analogy 
19:49  
 
Example 1: The Standard Normal Distribution and ZScores 
22:34  
 
Example 2: The Standard Normal Distribution and ZScores 
25:54  
 
Example 3: Sketching and Normal Distribution 
28:55  
 
Example 4: Sketching and Normal Distribution 
32:32  

Standard Normal Distributions & ZScores 
41:44 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
A Family of Distributions 
0:28  
 
 Infinite Set of Distributions 
0:29  
 
 Transforming Normal Distributions to 'Standard' Normal Distribution 
1:04  
 
Normal Distribution vs. Standard Normal Distribution 
2:58  
 
 Normal Distribution vs. Standard Normal Distribution 
2:59  
 
ZScore, Raw Score, Mean, & SD 
4:08  
 
 ZScore, Raw Score, Mean, & SD 
4:09  
 
Weird ZScores 
9:40  
 
 Weird ZScores 
9:41  
 
Excel 
16:45  
 
 For Normal Distributions 
16:46  
 
 For Standard Normal Distributions 
19:11  
 
 Excel Example 
20:24  
 
Types of Problems 
25:18  
 
 Percentage Problem: P(x) 
25:19  
 
 Raw Score and ZScore Problems 
26:28  
 
 Standard Deviation Problems 
27:01  
 
Shape Analogy 
27:44  
 
 Shape Analogy 
27:45  
 
Example 1: Deaths Due to Heart Disease vs. Deaths Due to Cancer 
28:24  
 
Example 2: Heights of Male College Students 
33:15  
 
Example 3: Mean and Standard Deviation 
37:14  
 
Example 4: Finding Percentage of Values in a Standard Normal Distribution 
37:49  

Normal Distribution: PDF vs. CDF 
55:44 
 
Intro 
0:00  
 
Roadmap 
0:15  
 
 Roadmap 
0:16  
 
Frequency vs. Cumulative Frequency 
0:56  
 
 Frequency vs. Cumulative Frequency 
0:57  
 
Frequency vs. Cumulative Frequency 
4:32  
 
 Frequency vs. Cumulative Frequency Cont. 
4:33  
 
Calculus in Brief 
6:21  
 
 DerivativeIntegral Continuum 
6:22  
 
PDF 
10:08  
 
 PDF for Standard Normal Distribution 
10:09  
 
 PDF for Normal Distribution 
14:32  
 
Integral of PDF = CDF 
21:27  
 
 Integral of PDF = CDF 
21:28  
 
Example 1: Cumulative Frequency Graph 
23:31  
 
Example 2: Mean, Standard Deviation, and Probability 
24:43  
 
Example 3: Mean and Standard Deviation 
35:50  
 
Example 4: Age of Cars 
49:32  
V. Linear Regression 

Scatterplots 
47:19 
 
Intro 
0:00  
 
Roadmap 
0:04  
 
 Roadmap 
0:05  
 
Previous Visualizations 
0:30  
 
 Frequency Distributions 
0:31  
 
Compare & Contrast 
2:26  
 
 Frequency Distributions Vs. Scatterplots 
2:27  
 
Summary Values 
4:53  
 
 Shape 
4:54  
 
 Center & Trend 
6:41  
 
 Spread & Strength 
8:22  
 
 Univariate & Bivariate 
10:25  
 
Example Scatterplot 
10:48  
 
 Shape, Trend, and Strength 
10:49  
 
Positive and Negative Association 
14:05  
 
 Positive and Negative Association 
14:06  
 
Linearity, Strength, and Consistency 
18:30  
 
 Linearity 
18:31  
 
 Strength 
19:14  
 
 Consistency 
20:40  
 
Summarizing a Scatterplot 
22:58  
 
 Summarizing a Scatterplot 
22:59  
 
Example 1: Gapminder.org, Income x Life Expectancy 
26:32  
 
Example 2: Gapminder.org, Income x Infant Mortality 
36:12  
 
Example 3: Trend and Strength of Variables 
40:14  
 
Example 4: Trend, Strength and Shape for Scatterplots 
43:27  

Regression 
32:02 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Linear Equations 
0:34  
 
 Linear Equations: y = mx + b 
0:35  
 
Rough Line 
5:16  
 
 Rough Line 
5:17  
 
Regression  A 'Center' Line 
7:41  
 
 Reasons for Summarizing with a Regression Line 
7:42  
 
 Predictor and Response Variable 
10:04  
 
Goal of Regression 
12:29  
 
 Goal of Regression 
12:30  
 
Prediction 
14:50  
 
 Example: Servings of Mile Per Year Shown By Age 
14:51  
 
 Intrapolation 
17:06  
 
 Extrapolation 
17:58  
 
Error in Prediction 
20:34  
 
 Prediction Error 
20:35  
 
 Residual 
21:40  
 
Example 1: Residual 
23:34  
 
Example 2: Large and Negative Residual 
26:30  
 
Example 3: Positive Residual 
28:13  
 
Example 4: Interpret Regression Line & Extrapolate 
29:40  

Least Squares Regression 
56:36 
 
Intro 
0:00  
 
Roadmap 
0:13  
 
 Roadmap 
0:14  
 
Best Fit 
0:47  
 
 Best Fit 
0:48  
 
Sum of Squared Errors (SSE) 
1:50  
 
 Sum of Squared Errors (SSE) 
1:51  
 
Why Squared? 
3:38  
 
 Why Squared? 
3:39  
 
Quantitative Properties of Regression Line 
4:51  
 
 Quantitative Properties of Regression Line 
4:52  
 
So How do we Find Such a Line? 
6:49  
 
 SSEs of Different Line Equations & Lowest SSE 
6:50  
 
 Carl Gauss' Method 
8:01  
 
How Do We Find Slope (b1) 
11:00  
 
 How Do We Find Slope (b1) 
11:01  
 
Hoe Do We Find Intercept 
15:11  
 
 Hoe Do We Find Intercept 
15:12  
 
Example 1: Which of These Equations Fit the Above Data Best? 
17:18  
 
Example 2: Find the Regression Line for These Data Points and Interpret It 
26:31  
 
Example 3: Summarize the Scatterplot and Find the Regression Line. 
34:31  
 
Example 4: Examine the Mean of Residuals 
43:52  

Correlation 
43:58 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Summarizing a Scatterplot Quantitatively 
0:47  
 
 Shape 
0:48  
 
 Trend 
1:11  
 
 Strength: Correlation ® 
1:45  
 
Correlation Coefficient ( r ) 
2:30  
 
 Correlation Coefficient ( r ) 
2:31  
 
Trees vs. Forest 
11:59  
 
 Trees vs. Forest 
12:00  
 
Calculating r 
15:07  
 
 Average Product of zscores for x and y 
15:08  
 
Relationship between Correlation and Slope 
21:10  
 
 Relationship between Correlation and Slope 
21:11  
 
Example 1: Find the Correlation between Grams of Fat and Cost 
24:11  
 
Example 2: Relationship between r and b1 
30:24  
 
Example 3: Find the Regression Line 
33:35  
 
Example 4: Find the Correlation Coefficient for this Set of Data 
37:37  

Correlation: r vs. rsquared 
52:52 
 
Intro 
0:00  
 
Roadmap 
0:07  
 
 Roadmap 
0:08  
 
Rsquared 
0:44  
 
 What is the Meaning of It? Why Squared? 
0:45  
 
Parsing Sum of Squared (Parsing Variability) 
2:25  
 
 SST = SSR + SSE 
2:26  
 
What is SST and SSE? 
7:46  
 
 What is SST and SSE? 
7:47  
 
rsquared 
18:33  
 
 Coefficient of Determination 
18:34  
 
If the Correlation is Strong… 
20:25  
 
 If the Correlation is Strong… 
20:26  
 
If the Correlation is Weak… 
22:36  
 
 If the Correlation is Weak… 
22:37  
 
Example 1: Find rsquared for this Set of Data 
23:56  
 
Example 2: What Does it Mean that the Simple Linear Regression is a 'Model' of Variance? 
33:54  
 
Example 3: Why Does rsquared Only Range from 0 to 1 
37:29  
 
Example 4: Find the rsquared for This Set of Data 
39:55  

Transformations of Data 
27:08 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Why Transform? 
0:26  
 
 Why Transform? 
0:27  
 
Shapepreserving vs. Shapechanging Transformations 
5:14  
 
 Shapepreserving = Linear Transformations 
5:15  
 
 Shapechanging Transformations = Nonlinear Transformations 
6:20  
 
Common ShapePreserving Transformations 
7:08  
 
 Common ShapePreserving Transformations 
7:09  
 
Common ShapeChanging Transformations 
8:59  
 
 Powers 
9:00  
 
 Logarithms 
9:39  
 
Change Just One Variable? Both? 
10:38  
 
 Loglog Transformations 
10:39  
 
 Log Transformations 
14:38  
 
Example 1: Create, Graph, and Transform the Data Set 
15:19  
 
Example 2: Create, Graph, and Transform the Data Set 
20:08  
 
Example 3: What Kind of Model would You Choose for this Data? 
22:44  
 
Example 4: Transformation of Data 
25:46  
VI. Collecting Data in an Experiment 

Sampling & Bias 
54:44 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Descriptive vs. Inferential Statistics 
1:04  
 
 Descriptive Statistics: Data Exploration 
1:05  
 
 Example 
2:03  
 
To tackle Generalization… 
4:31  
 
 Generalization 
4:32  
 
 Sampling 
6:06  
 
 'Good' Sample 
6:40  
 
Defining Samples and Populations 
8:55  
 
 Population 
8:56  
 
 Sample 
11:16  
 
Why Use Sampling? 
13:09  
 
 Why Use Sampling? 
13:10  
 
Goal of Sampling: Avoiding Bias 
15:04  
 
 What is Bias? 
15:05  
 
 Where does Bias Come from: Sampling Bias 
17:53  
 
 Where does Bias Come from: Response Bias 
18:27  
 
Sampling Bias: Bias from Bas Sampling Methods 
19:34  
 
 Size Bias 
19:35  
 
 Voluntary Response Bias 
21:13  
 
 Convenience Sample 
22:22  
 
 Judgment Sample 
23:58  
 
 Inadequate Sample Frame 
25:40  
 
Response Bias: Bias from 'Bad' Data Collection Methods 
28:00  
 
 Nonresponse Bias 
29:31  
 
 Questionnaire Bias 
31:10  
 
 Incorrect Response or Measurement Bias 
37:32  
 
Example 1: What Kind of Biases? 
40:29  
 
Example 2: What Biases Might Arise? 
44:46  
 
Example 3: What Kind of Biases? 
48:34  
 
Example 4: What Kind of Biases? 
51:43  

Sampling Methods 
14:25 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Biased vs. Unbiased Sampling Methods 
0:32  
 
 Biased Sampling 
0:33  
 
 Unbiased Sampling 
1:13  
 
Probability Sampling Methods 
2:31  
 
 Simple Random 
2:54  
 
 Stratified Random Sampling 
4:06  
 
 Cluster Sampling 
5:24  
 
 Twostaged Sampling 
6:22  
 
 Systematic Sampling 
7:25  
 
Example 1: Which Type(s) of Sampling was this? 
8:33  
 
Example 2: Describe How to Take a TwoStage Sample from this Book 
10:16  
 
Example 3: Sampling Methods 
11:58  
 
Example 4: Cluster Sample Plan 
12:48  

Research Design 
53:54 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
Descriptive vs. Inferential Statistics 
0:51  
 
 Descriptive Statistics: Data Exploration 
0:52  
 
 Inferential Statistics 
1:02  
 
Variables and Relationships 
1:44  
 
 Variables 
1:45  
 
 Relationships 
2:49  
 
Not Every Type of Study is an Experiment… 
4:16  
 
 Category I  Descriptive Study 
4:54  
 
 Category II  Correlational Study 
5:50  
 
 Category III  Experimental, Quasiexperimental, Nonexperimental 
6:33  
 
Category III 
7:42  
 
 Experimental, Quasiexperimental, and Nonexperimental 
7:43  
 
Why CAN'T the Other Strategies Determine Causation? 
10:18  
 
 Thirdvariable Problem 
10:19  
 
 Directionality Problem 
15:49  
 
What Makes Experiments Special? 
17:54  
 
 Manipulation 
17:55  
 
 Control (and Comparison) 
21:58  
 
Methods of Control 
26:38  
 
 Holding Constant 
26:39  
 
 Matching 
29:11  
 
 Random Assignment 
31:48  
 
Experiment Terminology 
34:09  
 
 'true' Experiment vs. Study 
34:10  
 
 Independent Variable (IV) 
35:16  
 
 Dependent Variable (DV) 
35:45  
 
 Factors 
36:07  
 
 Treatment Conditions 
36:23  
 
 Levels 
37:43  
 
 Confounds or Extraneous Variables 
38:04  
 
Blind 
38:38  
 
 Blind Experiments 
38:39  
 
 Doubleblind Experiments 
39:29  
 
How Categories Relate to Statistics 
41:35  
 
 Category I  Descriptive Study 
41:36  
 
 Category II  Correlational Study 
42:05  
 
 Category III  Experimental, Quasiexperimental, Nonexperimental 
42:43  
 
Example 1: Research Design 
43:50  
 
Example 2: Research Design 
47:37  
 
Example 3: Research Design 
50:12  
 
Example 4: Research Design 
52:00  

Between and Within Treatment Variability 
41:31 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
Experimental Designs 
0:51  
 
 Experimental Designs: Manipulation & Control 
0:52  
 
Two Types of Variability 
2:09  
 
 Between Treatment Variability 
2:10  
 
 Within Treatment Variability 
3:31  
 
Updated Goal of Experimental Design 
5:47  
 
 Updated Goal of Experimental Design 
5:48  
 
Example: Drugs and Driving 
6:56  
 
 Example: Drugs and Driving 
6:57  
 
Different Types of Random Assignment 
11:27  
 
 All Experiments 
11:28  
 
 Completely Random Design 
12:02  
 
 Randomized Block Design 
13:19  
 
Randomized Block Design 
15:48  
 
 Matched Pairs Design 
15:49  
 
 Repeated Measures Design 
19:47  
 
Betweensubject Variable vs. Withinsubject Variable 
22:43  
 
 Completely Randomized Design 
22:44  
 
 Repeated Measures Design 
25:03  
 
Example 1: Design a Completely Random, Matched Pair, and Repeated Measures Experiment 
26:16  
 
Example 2: Block Design 
31:41  
 
Example 3: Completely Randomized Designs 
35:11  
 
Example 4: Completely Random, Matched Pairs, or Repeated Measures Experiments? 
39:01  
VII. Review of Probability Axioms 

Sample Spaces 
37:52 
 
Intro 
0:00  
 
Roadmap 
0:07  
 
 Roadmap 
0:08  
 
Why is Probability Involved in Statistics 
0:48  
 
 Probability 
0:49  
 
 Can People Tell the Difference between Cheap and Gourmet Coffee? 
2:08  
 
Taste Test with Coffee Drinkers 
3:37  
 
 If No One can Actually Taste the Difference 
3:38  
 
 If Everyone can Actually Taste the Difference 
5:36  
 
Creating a Probability Model 
7:09  
 
 Creating a Probability Model 
7:10  
 
D'Alembert vs. Necker 
9:41  
 
 D'Alembert vs. Necker 
9:42  
 
Problem with D'Alembert's Model 
13:29  
 
 Problem with D'Alembert's Model 
13:30  
 
Covering Entire Sample Space 
15:08  
 
 Fundamental Principle of Counting 
15:09  
 
Where Do Probabilities Come From? 
22:54  
 
 Observed Data, Symmetry, and Subjective Estimates 
22:55  
 
Checking whether Model Matches Real World 
24:27  
 
 Law of Large Numbers 
24:28  
 
Example 1: Law of Large Numbers 
27:46  
 
Example 2: Possible Outcomes 
30:43  
 
Example 3: Brands of Coffee and Taste 
33:25  
 
Example 4: How Many Different Treatments are there? 
35:33  

Addition Rule for Disjoint Events 
20:29 
 
Intro 
0:00  
 
Roadmap 
0:08  
 
 Roadmap 
0:09  
 
Disjoint Events 
0:41  
 
 Disjoint Events 
0:42  
 
Meaning of 'or' 
2:39  
 
 In Regular Life 
2:40  
 
 In Math/Statistics/Computer Science 
3:10  
 
Addition Rule for Disjoin Events 
3:55  
 
 If A and B are Disjoint: P (A and B) 
3:56  
 
 If A and B are Disjoint: P (A or B) 
5:15  
 
General Addition Rule 
5:41  
 
 General Addition Rule 
5:42  
 
Generalized Addition Rule 
8:31  
 
 If A and B are not Disjoint: P (A or B) 
8:32  
 
Example 1: Which of These are Mutually Exclusive? 
10:50  
 
Example 2: What is the Probability that You will Have a Combination of One Heads and Two Tails? 
12:57  
 
Example 3: Engagement Party 
15:17  
 
Example 4: Home Owner's Insurance 
18:30  

Conditional Probability 
57:19 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
'or' vs. 'and' vs. Conditional Probability 
1:07  
 
 'or' vs. 'and' vs. Conditional Probability 
1:08  
 
'and' vs. Conditional Probability 
5:57  
 
 P (M or L) 
5:58  
 
 P (M and L) 
8:41  
 
 P (ML) 
11:04  
 
 P (LM) 
12:24  
 
Tree Diagram 
15:02  
 
 Tree Diagram 
15:03  
 
Defining Conditional Probability 
22:42  
 
 Defining Conditional Probability 
22:43  
 
Common Contexts for Conditional Probability 
30:56  
 
 Medical Testing: Positive Predictive Value 
30:57  
 
 Medical Testing: Sensitivity 
33:03  
 
 Statistical Tests 
34:27  
 
Example 1: Drug and Disease 
36:41  
 
Example 2: Marbles and Conditional Probability 
40:04  
 
Example 3: Cards and Conditional Probability 
45:59  
 
Example 4: Votes and Conditional Probability 
50:21  

Independent Events 
24:27 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Independent Events & Conditional Probability 
0:26  
 
 Nonindependent Events 
0:27  
 
 Independent Events 
2:00  
 
Nonindependent and Independent Events 
3:08  
 
 Nonindependent and Independent Events 
3:09  
 
Defining Independent Events 
5:52  
 
 Defining Independent Events 
5:53  
 
Multiplication Rule 
7:29  
 
 Previously… 
7:30  
 
 But with Independent Evens 
8:53  
 
Example 1: Which of These Pairs of Events are Independent? 
11:12  
 
Example 2: Health Insurance and Probability 
15:12  
 
Example 3: Independent Events 
17:42  
 
Example 4: Independent Events 
20:03  
VIII. Probability Distributions 

Introduction to Probability Distributions 
56:45 
 
Intro 
0:00  
 
Roadmap 
0:08  
 
 Roadmap 
0:09  
 
Sampling vs. Probability 
0:57  
 
 Sampling 
0:58  
 
 Missing 
1:30  
 
 What is Missing? 
3:06  
 
Insight: Probability Distributions 
5:26  
 
 Insight: Probability Distributions 
5:27  
 
 What is a Probability Distribution? 
7:29  
 
From Sample Spaces to Probability Distributions 
8:44  
 
 Sample Space 
8:45  
 
 Probability Distribution of the Sum of Two Die 
11:16  
 
The Random Variable 
17:43  
 
 The Random Variable 
17:44  
 
Expected Value 
21:52  
 
 Expected Value 
21:53  
 
Example 1: Probability Distributions 
28:45  
 
Example 2: Probability Distributions 
35:30  
 
Example 3: Probability Distributions 
43:37  
 
Example 4: Probability Distributions 
47:20  

Expected Value & Variance of Probability Distributions 
53:41 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
Discrete vs. Continuous Random Variables 
1:04  
 
 Discrete vs. Continuous Random Variables 
1:05  
 
Mean and Variance Review 
4:44  
 
 Mean: Sample, Population, and Probability Distribution 
4:45  
 
 Variance: Sample, Population, and Probability Distribution 
9:12  
 
Example Situation 
14:10  
 
 Example Situation 
14:11  
 
Some Special Cases… 
16:13  
 
 Some Special Cases… 
16:14  
 
Linear Transformations 
19:22  
 
 Linear Transformations 
19:23  
 
 What Happens to Mean and Variance of the Probability Distribution? 
20:12  
 
n Independent Values of X 
25:38  
 
 n Independent Values of X 
25:39  
 
Compare These Two Situations 
30:56  
 
 Compare These Two Situations 
30:57  
 
Two Random Variables, X and Y 
32:02  
 
 Two Random Variables, X and Y 
32:03  
 
Example 1: Expected Value & Variance of Probability Distributions 
35:35  
 
Example 2: Expected Values & Standard Deviation 
44:17  
 
Example 3: Expected Winnings and Standard Deviation 
48:18  

Binomial Distribution 
55:15 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Discrete Probability Distributions 
1:42  
 
 Discrete Probability Distributions 
1:43  
 
Binomial Distribution 
2:36  
 
 Binomial Distribution 
2:37  
 
Multiplicative Rule Review 
6:54  
 
 Multiplicative Rule Review 
6:55  
 
How Many Outcomes with k 'Successes' 
10:23  
 
 Adults and Bachelor's Degree: Manual List of Outcomes 
10:24  
 
P (X=k) 
19:37  
 
 Putting Together # of Outcomes with the Multiplicative Rule 
19:38  
 
Expected Value and Standard Deviation in a Binomial Distribution 
25:22  
 
 Expected Value and Standard Deviation in a Binomial Distribution 
25:23  
 
Example 1: Coin Toss 
33:42  
 
Example 2: College Graduates 
38:03  
 
Example 3: Types of Blood and Probability 
45:39  
 
Example 4: Expected Number and Standard Deviation 
51:11  
IX. Sampling Distributions of Statistics 

Introduction to Sampling Distributions 
48:17 
 
Intro 
0:00  
 
Roadmap 
0:08  
 
 Roadmap 
0:09  
 
Probability Distributions vs. Sampling Distributions 
0:55  
 
 Probability Distributions vs. Sampling Distributions 
0:56  
 
Same Logic 
3:55  
 
 Logic of Probability Distribution 
3:56  
 
 Example: Rolling Two Die 
6:56  
 
Simulating Samples 
9:53  
 
 To Come Up with Probability Distributions 
9:54  
 
 In Sampling Distributions 
11:12  
 
Connecting Sampling and Research Methods with Sampling Distributions 
12:11  
 
 Connecting Sampling and Research Methods with Sampling Distributions 
12:12  
 
Simulating a Sampling Distribution 
14:14  
 
 Experimental Design: Regular Sleep vs. Less Sleep 
14:15  
 
Logic of Sampling Distributions 
23:08  
 
 Logic of Sampling Distributions 
23:09  
 
General Method of Simulating Sampling Distributions 
25:38  
 
 General Method of Simulating Sampling Distributions 
25:39  
 
Questions that Remain 
28:45  
 
 Questions that Remain 
28:46  
 
Example 1: Mean and Standard Error of Sampling Distribution 
30:57  
 
Example 2: What is the Best Way to Describe Sampling Distributions? 
37:12  
 
Example 3: Matching Sampling Distributions 
38:21  
 
Example 4: Mean and Standard Error of Sampling Distribution 
41:51  

Sampling Distribution of the Mean 
68:48 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Special Case of General Method for Simulating a Sampling Distribution 
1:53  
 
 Special Case of General Method for Simulating a Sampling Distribution 
1:54  
 
 Computer Simulation 
3:43  
 
Using Simulations to See Principles behind Shape of SDoM 
15:50  
 
 Using Simulations to See Principles behind Shape of SDoM 
15:51  
 
 Conditions 
17:38  
 
Using Simulations to See Principles behind Center (Mean) of SDoM 
20:15  
 
 Using Simulations to See Principles behind Center (Mean) of SDoM 
20:16  
 
 Conditions: Does n Matter? 
21:31  
 
 Conditions: Does Number of Simulation Matter? 
24:37  
 
Using Simulations to See Principles behind Standard Deviation of SDoM 
27:13  
 
 Using Simulations to See Principles behind Standard Deviation of SDoM 
27:14  
 
 Conditions: Does n Matter? 
34:45  
 
 Conditions: Does Number of Simulation Matter? 
36:24  
 
Central Limit Theorem 
37:13  
 
 SHAPE 
38:08  
 
 CENTER 
39:34  
 
 SPREAD 
39:52  
 
Comparing Population, Sample, and SDoM 
43:10  
 
 Comparing Population, Sample, and SDoM 
43:11  
 
Answering the 'Questions that Remain' 
48:24  
 
 What Happens When We Don't Know What the Population Looks Like? 
48:25  
 
 Can We Have Sampling Distributions for Summary Statistics Other than the Mean? 
49:42  
 
 How Do We Know whether a Sample is Sufficiently Unlikely? 
53:36  
 
 Do We Always Have to Simulate a Large Number of Samples in Order to get a Sampling Distribution? 
54:40  
 
Example 1: Mean Batting Average 
55:25  
 
Example 2: Mean Sampling Distribution and Standard Error 
59:07  
 
Example 3: Sampling Distribution of the Mean 
61:04  

Sampling Distribution of Sample Proportions 
54:37 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
Intro to Sampling Distribution of Sample Proportions (SDoSP) 
0:51  
 
 Categorical Data (Examples) 
0:52  
 
 Wish to Estimate Proportion of Population from Sample… 
2:00  
 
Notation 
3:34  
 
 Population Proportion and Sample Proportion Notations 
3:35  
 
What's the Difference? 
9:19  
 
 SDoM vs. SDoSP: Type of Data 
9:20  
 
 SDoM vs. SDoSP: Shape 
11:24  
 
 SDoM vs. SDoSP: Center 
12:30  
 
 SDoM vs. SDoSP: Spread 
15:34  
 
Binomial Distribution vs. Sampling Distribution of Sample Proportions 
19:14  
 
 Binomial Distribution vs. SDoSP: Type of Data 
19:17  
 
 Binomial Distribution vs. SDoSP: Shape 
21:07  
 
 Binomial Distribution vs. SDoSP: Center 
21:43  
 
 Binomial Distribution vs. SDoSP: Spread 
24:08  
 
Example 1: Sampling Distribution of Sample Proportions 
26:07  
 
Example 2: Sampling Distribution of Sample Proportions 
37:58  
 
Example 3: Sampling Distribution of Sample Proportions 
44:42  
 
Example 4: Sampling Distribution of Sample Proportions 
45:57  
X. Inferential Statistics 

Introduction to Confidence Intervals 
42:53 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
Inferential Statistics 
0:50  
 
 Inferential Statistics 
0:51  
 
Two Problems with This Picture… 
3:20  
 
 Two Problems with This Picture… 
3:21  
 
 Solution: Confidence Intervals (CI) 
4:59  
 
 Solution: Hypotheiss Testing (HT) 
5:49  
 
Which Parameters are Known? 
6:45  
 
 Which Parameters are Known? 
6:46  
 
Confidence Interval  Goal 
7:56  
 
 When We Don't Know m but know s 
7:57  
 
When We Don't Know 
18:27  
 
 When We Don't Know m nor s 
18:28  
 
Example 1: Confidence Intervals 
26:18  
 
Example 2: Confidence Intervals 
29:46  
 
Example 3: Confidence Intervals 
32:18  
 
Example 4: Confidence Intervals 
38:31  

t Distributions 
62:06 
 
Intro 
0:00  
 
Roadmap 
0:04  
 
 Roadmap 
0:05  
 
When to Use z vs. t? 
1:07  
 
 When to Use z vs. t? 
1:08  
 
What is z and t? 
3:02  
 
 zscore and tscore: Commonality 
3:03  
 
 zscore and tscore: Formulas 
3:34  
 
 zscore and tscore: Difference 
5:22  
 
Why not z? (Why t?) 
7:24  
 
 Why not z? (Why t?) 
7:25  
 
But Don't Worry! 
15:13  
 
 Gossett and tdistributions 
15:14  
 
Rules of t Distributions 
17:05  
 
 tdistributions are More Normal as n Gets Bigger 
17:06  
 
 tdistributions are a Family of Distributions 
18:55  
 
Degrees of Freedom (df) 
20:02  
 
 Degrees of Freedom (df) 
20:03  
 
t Family of Distributions 
24:07  
 
 t Family of Distributions : df = 2 , 4, and 60 
24:08  
 
 df = 60 
29:16  
 
 df = 2 
29:59  
 
How to Find It? 
31:01  
 
 'Student's tdistribution' or 'tdistribution' 
31:02  
 
 Excel Example 
33:06  
 
Example 1: Which Distribution Do You Use? Z or t? 
45:26  
 
Example 2: Friends on Facebook 
47:41  
 
Example 3: t Distributions 
52:15  
 
Example 4: t Distributions , confidence interval, and mean 
55:59  

Introduction to Hypothesis Testing 
66:33 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
Issues to Overcome in Inferential Statistics 
1:35  
 
 Issues to Overcome in Inferential Statistics 
1:36  
 
 What Happens When We Don't Know What the Population Looks Like? 
2:57  
 
 How Do We Know whether a sample is Sufficiently Unlikely 
3:43  
 
Hypothesizing a Population 
6:44  
 
 Hypothesizing a Population 
6:45  
 
 Null Hypothesis 
8:07  
 
 Alternative Hypothesis 
8:56  
 
Hypotheses 
11:58  
 
 Hypotheses 
11:59  
 
Errors in Hypothesis Testing 
14:22  
 
 Errors in Hypothesis Testing 
14:23  
 
Steps of Hypothesis Testing 
21:15  
 
 Steps of Hypothesis Testing 
21:16  
 
Single Sample HT ( When Sigma Available) 
26:08  
 
 Example: Average Facebook Friends 
26:09  
 
 Step1 
27:08  
 
 Step 2 
27:58  
 
 Step 3 
28:17  
 
 Step 4 
32:18  
 
Single Sample HT (When Sigma Not Available) 
36:33  
 
 Example: Average Facebook Friends 
36:34  
 
 Step1: Hypothesis Testing 
36:58  
 
 Step 2: Significance Level 
37:25  
 
 Step 3: Decision Stage 
37:40  
 
 Step 4: Sample 
41:36  
 
Sigma and pvalue 
45:04  
 
 Sigma and pvalue 
45:05  
 
 On tailed vs. Two Tailed Hypotheses 
45:51  
 
Example 1: Hypothesis Testing 
48:37  
 
Example 2: Heights of Women in the US 
57:43  
 
Example 3: Select the Best Way to Complete This Sentence 
63:23  

Confidence Intervals for the Difference of Two Independent Means 
55:14 
 
Intro 
0:00  
 
Roadmap 
0:14  
 
 Roadmap 
0:15  
 
One Mean vs. Two Means 
1:17  
 
 One Mean vs. Two Means 
1:18  
 
Notation 
2:41  
 
 A Sample! A Set! 
2:42  
 
 Mean of X, Mean of Y, and Difference of Two Means 
3:56  
 
 SE of X 
4:34  
 
 SE of Y 
6:28  
 
Sampling Distribution of the Difference between Two Means (SDoD) 
7:48  
 
 Sampling Distribution of the Difference between Two Means (SDoD) 
7:49  
 
Rules of the SDoD (similar to CLT!) 
15:00  
 
 Mean for the SDoD Null Hypothesis 
15:01  
 
 Standard Error 
17:39  
 
When can We Construct a CI for the Difference between Two Means? 
21:28  
 
 Three Conditions 
21:29  
 
Finding CI 
23:56  
 
 One Mean CI 
23:57  
 
 Two Means CI 
25:45  
 
Finding t 
29:16  
 
 Finding t 
29:17  
 
Interpreting CI 
30:25  
 
 Interpreting CI 
30:26  
 
Better Estimate of s (s pool) 
34:15  
 
 Better Estimate of s (s pool) 
34:16  
 
Example 1: Confidence Intervals 
42:32  
 
Example 2: SE of the Difference 
52:36  

Hypothesis Testing for the Difference of Two Independent Means 
50:00 
 
Intro 
0:00  
 
Roadmap 
0:06  
 
 Roadmap 
0:07  
 
The Goal of Hypothesis Testing 
0:56  
 
 One Sample and Two Samples 
0:57  
 
Sampling Distribution of the Difference between Two Means (SDoD) 
3:42  
 
 Sampling Distribution of the Difference between Two Means (SDoD) 
3:43  
 
Rules of the SDoD (Similar to CLT!) 
6:46  
 
 Shape 
6:47  
 
 Mean for the Null Hypothesis 
7:26  
 
 Standard Error for Independent Samples (When Variance is Homogenous) 
8:18  
 
 Standard Error for Independent Samples (When Variance is not Homogenous) 
9:25  
 
Same Conditions for HT as for CI 
10:08  
 
 Three Conditions 
10:09  
 
Steps of Hypothesis Testing 
11:04  
 
 Steps of Hypothesis Testing 
11:05  
 
Formulas that Go with Steps of Hypothesis Testing 
13:21  
 
 Step 1 
13:25  
 
 Step 2 
14:18  
 
 Step 3 
15:00  
 
 Step 4 
16:57  
 
Example 1: Hypothesis Testing for the Difference of Two Independent Means 
18:47  
 
Example 2: Hypothesis Testing for the Difference of Two Independent Means 
33:55  
 
Example 3: Hypothesis Testing for the Difference of Two Independent Means 
44:22  

Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means 
74:11 
 
Intro 
0:00  
 
Roadmap 
0:09  
 
 Roadmap 
0:10  
 
The Goal of Hypothesis Testing 
1:27  
 
 One Sample and Two Samples 
1:28  
 
Independent Samples vs. Paired Samples 
3:16  
 
 Independent Samples vs. Paired Samples 
3:17  
 
 Which is Which? 
5:20  
 
Independent SAMPLES vs. Independent VARIABLES 
7:43  
 
 independent SAMPLES vs. Independent VARIABLES 
7:44  
 
Ttests Always… 
10:48  
 
 Ttests Always… 
10:49  
 
Notation for Paired Samples 
12:59  
 
 Notation for Paired Samples 
13:00  
 
Steps of Hypothesis Testing for Paired Samples 
16:13  
 
 Steps of Hypothesis Testing for Paired Samples 
16:14  
 
Rules of the SDoD (Adding on Paired Samples) 
18:03  
 
 Shape 
18:04  
 
 Mean for the Null Hypothesis 
18:31  
 
 Standard Error for Independent Samples (When Variance is Homogenous) 
19:25  
 
 Standard Error for Paired Samples 
20:39  
 
Formulas that go with Steps of Hypothesis Testing 
22:59  
 
 Formulas that go with Steps of Hypothesis Testing 
23:00  
 
Confidence Intervals for Paired Samples 
30:32  
 
 Confidence Intervals for Paired Samples 
30:33  
 
Example 1: Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means 
32:28  
 
Example 2: Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means 
44:02  
 
Example 3: Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means 
52:23  

Type I and Type II Errors 
31:27 
 
Intro 
0:00  
 
Roadmap 
0:18  
 
 Roadmap 
0:19  
 
Errors and Relationship to HT and the Sample Statistic? 
1:11  
 
 Errors and Relationship to HT and the Sample Statistic? 
1:12  
 
Instead of a Box…Distributions! 
7:00  
 
 One Sample ttest: Friends on Facebook 
70:1  
 
 Two Sample ttest: Friends on Facebook 
13:46  
 
Usually, Lots of Overlap between Null and Alternative Distributions 
16:59  
 
 Overlap between Null and Alternative Distributions 
17:00  
 
How Distributions and 'Box' Fit Together 
22:45  
 
 How Distributions and 'Box' Fit Together 
22:46  
 
Example 1: Types of Errors 
25:54  
 
Example 2: Types of Errors 
27:30  
 
Example 3: What is the Danger of the Type I Error? 
29:38  

Effect Size & Power 
44:41 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Distance between Distributions: Sample t 
0:49  
 
 Distance between Distributions: Sample t 
0:50  
 
Problem with Distance in Terms of Standard Error 
2:56  
 
 Problem with Distance in Terms of Standard Error 
2:57  
 
Test Statistic (t) vs. Effect Size (d or g) 
4:38  
 
 Test Statistic (t) vs. Effect Size (d or g) 
4:39  
 
Rules of Effect Size 
6:09  
 
 Rules of Effect Size 
6:10  
 
Why Do We Need Effect Size? 
8:21  
 
 Tells You the Practical Significance 
8:22  
 
 HT can be Deceiving… 
10:25  
 
 Important Note 
10:42  
 
What is Power? 
11:20  
 
 What is Power? 
11:21  
 
Why Do We Need Power? 
14:19  
 
 Conditional Probability and Power 
14:20  
 
 Power is: 
16:27  
 
Can We Calculate Power? 
19:00  
 
 Can We Calculate Power? 
19:01  
 
How Does Alpha Affect Power? 
20:36  
 
 How Does Alpha Affect Power? 
20:37  
 
How Does Effect Size Affect Power? 
25:38  
 
 How Does Effect Size Affect Power? 
25:39  
 
How Does Variability and Sample Size Affect Power? 
27:56  
 
 How Does Variability and Sample Size Affect Power? 
27:57  
 
How Do We Increase Power? 
32:47  
 
 Increasing Power 
32:48  
 
Example 1: Effect Size & Power 
35:40  
 
Example 2: Effect Size & Power 
37:38  
 
Example 3: Effect Size & Power 
40:55  
XI. Analysis of Variance 

Fdistributions 
24:46 
 
Intro 
0:00  
 
Roadmap 
0:04  
 
 Roadmap 
0:05  
 
Z & Tstatistic and Their Distribution 
0:34  
 
 Z & Tstatistic and Their Distribution 
0:35  
 
Fstatistic 
4:55  
 
 The F Ration ( the Variance Ratio) 
4:56  
 
Fdistribution 
12:29  
 
 Fdistribution 
12:30  
 
s and pvalue 
15:00  
 
 s and pvalue 
15:01  
 
Example 1: Why Does Fdistribution Stop At 0 But Go On Until Infinity? 
18:33  
 
Example 2: Fdistributions 
19:29  
 
Example 3: Fdistributions and Heights 
21:29  

ANOVA with Independent Samples 
69:25 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
The Limitations of ttests 
1:12  
 
 The Limitations of ttests 
1:13  
 
Two Major Limitations of Many ttests 
3:26  
 
 Two Major Limitations of Many ttests 
3:27  
 
Ronald Fisher's Solution… Ftest! New Null Hypothesis 
4:43  
 
 Ronald Fisher's Solution… Ftest! New Null Hypothesis (Omnibus Test  One Test to Rule Them All!) 
4:44  
 
Analysis of Variance (ANoVA) Notation 
7:47  
 
 Analysis of Variance (ANoVA) Notation 
7:48  
 
Partitioning (Analyzing) Variance 
9:58  
 
 Total Variance 
9:59  
 
 Withingroup Variation 
14:00  
 
 Betweengroup Variation 
16:22  
 
Time out: Review Variance & SS 
17:05  
 
 Time out: Review Variance & SS 
17:06  
 
Fstatistic 
19:22  
 
 The F Ratio (the Variance Ratio) 
19:23  
 
S²bet = SSbet / dfbet 
22:13  
 
 What is This? 
22:14  
 
 How Many Means? 
23:20  
 
 So What is the dfbet? 
23:38  
 
 So What is SSbet? 
24:15  
 
S²w = SSw / dfw 
26:05  
 
 What is This? 
26:06  
 
 How Many Means? 
27:20  
 
 So What is the dfw? 
27:36  
 
 So What is SSw? 
28:18  
 
Chart of Independent Samples ANOVA 
29:25  
 
 Chart of Independent Samples ANOVA 
29:26  
 
Example 1: Who Uploads More Photos: Unknown Ethnicity, Latino, Asian, Black, or White Facebook Users? 
35:52  
 
 Hypotheses 
35:53  
 
 Significance Level 
39:40  
 
 Decision Stage 
40:05  
 
 Calculate Samples' Statistic and pValue 
44:10  
 
 Reject or Fail to Reject H0 
55:54  
 
Example 2: ANOVA with Independent Samples 
58:21  

Repeated Measures ANOVA 
75:13 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
The Limitations of ttests 
0:36  
 
 Who Uploads more Pictures and Which PhotoType is Most Frequently Used on Facebook? 
0:37  
 
ANOVA (Ftest) to the Rescue! 
5:49  
 
 Omnibus Hypothesis 
5:50  
 
 Analyze Variance 
7:27  
 
Independent Samples vs. Repeated Measures 
9:12  
 
 Same Start 
9:13  
 
 Independent Samples ANOVA 
10:43  
 
 Repeated Measures ANOVA 
12:00  
 
Independent Samples ANOVA 
16:00  
 
 Same Start: All the Variance Around Grand Mean 
16:01  
 
 Independent Samples 
16:23  
 
Repeated Measures ANOVA 
18:18  
 
 Same Start: All the Variance Around Grand Mean 
18:19  
 
 Repeated Measures 
18:33  
 
Repeated Measures Fstatistic 
21:22  
 
 The F Ratio (The Variance Ratio) 
21:23  
 
S²bet = SSbet / dfbet 
23:07  
 
 What is This? 
23:08  
 
 How Many Means? 
23:39  
 
 So What is the dfbet? 
23:54  
 
 So What is SSbet? 
24:32  
 
S² resid = SS resid / df resid 
25:46  
 
 What is This? 
25:47  
 
 So What is SS resid? 
26:44  
 
 So What is the df resid? 
27:36  
 
SS subj and df subj 
28:11  
 
 What is This? 
28:12  
 
 How Many Subject Means? 
29:43  
 
 So What is df subj? 
30:01  
 
 So What is SS subj? 
30:09  
 
SS total and df total 
31:42  
 
 What is This? 
31:43  
 
 What is the Total Number of Data Points? 
32:02  
 
 So What is df total? 
32:34  
 
 so What is SS total? 
32:47  
 
Chart of Repeated Measures ANOVA 
33:19  
 
 Chart of Repeated Measures ANOVA: F and Betweensamples Variability 
33:20  
 
 Chart of Repeated Measures ANOVA: Total Variability, Withinsubject (case) Variability, Residual Variability 
35:50  
 
Example 1: Which is More Prevalent on Facebook: Tagged, Uploaded, Mobile, or Profile Photos? 
40:25  
 
 Hypotheses 
40:26  
 
 Significance Level 
41:46  
 
 Decision Stage 
42:09  
 
 Calculate Samples' Statistic and pValue 
46:18  
 
 Reject or Fail to Reject H0 
57:55  
 
Example 2: Repeated Measures ANOVA 
58:57  
 
Example 3: What's the Problem with a Bunch of Tiny ttests? 
73:59  
XII. Chisquare Test 

ChiSquare GoodnessofFit Test 
58:23 
 
Intro 
0:00  
 
Roadmap 
0:05  
 
 Roadmap 
0:06  
 
Where Does the ChiSquare Test Belong? 
0:50  
 
 Where Does the ChiSquare Test Belong? 
0:51  
 
A New Twist on HT: GoodnessofFit 
7:23  
 
 HT in General 
7:24  
 
 GoodnessofFit HT 
8:26  
 
Hypotheses about Proportions 
12:17  
 
 Null Hypothesis 
12:18  
 
 Alternative Hypothesis 
13:23  
 
 Example 
14:38  
 
ChiSquare Statistic 
17:52  
 
 ChiSquare Statistic 
17:53  
 
ChiSquare Distributions 
24:31  
 
 ChiSquare Distributions 
24:32  
 
Conditions for ChiSquare 
28:58  
 
 Condition 1 
28:59  
 
 Condition 2 
30:20  
 
 Condition 3 
30:32  
 
 Condition 4 
31:47  
 
Example 1: ChiSquare GoodnessofFit Test 
32:23  
 
Example 2: ChiSquare GoodnessofFit Test 
44:34  
 
Example 3: Which of These Statements Describe Properties of the ChiSquare GoodnessofFit Test? 
56:06  

ChiSquare Test of Homogeneity 
51:36 
 
Intro 
0:00  
 
Roadmap 
0:09  
 
 Roadmap 
0:10  
 
GoodnessofFit vs. Homogeneity 
1:13  
 
 GoodnessofFit HT 
1:14  
 
 Homogeneity 
2:00  
 
 Analogy 
2:38  
 
Hypotheses About Proportions 
5:00  
 
 Null Hypothesis 
5:01  
 
 Alternative Hypothesis 
6:11  
 
 Example 
6:33  
 
ChiSquare Statistic 
10:12  
 
 Same as GoodnessofFit Test 
10:13  
 
Set Up Data 
12:28  
 
 Setting Up Data Example 
12:29  
 
Expected Frequency 
16:53  
 
 Expected Frequency 
16:54  
 
ChiSquare Distributions & df 
19:26  
 
 ChiSquare Distributions & df 
19:27  
 
Conditions for Test of Homogeneity 
20:54  
 
 Condition 1 
20:55  
 
 Condition 2 
21:39  
 
 Condition 3 
22:05  
 
 Condition 4 
22:23  
 
Example 1: ChiSquare Test of Homogeneity 
22:52  
 
Example 2: ChiSquare Test of Homogeneity 
32:10  
XIII. Overview of Statistics 

Overview of Statistics 
18:11 
 
Intro 
0:00  
 
Roadmap 
0:07  
 
 Roadmap 
0:08  
 
The Statistical Tests (HT) We've Covered 
0:28  
 
 The Statistical Tests (HT) We've Covered 
0:29  
 
Organizing the Tests We've Covered… 
1:08  
 
 One Sample: Continuous DV and Categorical DV 
1:09  
 
 Two Samples: Continuous DV and Categorical DV 
5:41  
 
 More Than Two Samples: Continuous DV and Categorical DV 
8:21  
 
The Following Data: OK Cupid 
10:10  
 
 The Following Data: OK Cupid 
10:11  
 
Example 1: WeirdMySpaceAngle Profile Photo 
10:38  
 
Example 2: Geniuses 
12:30  
 
Example 3: Promiscuous iPhone Users 
13:37  
 
Example 4: Women, Aging, and Messaging 
16:07  