In Educator's General Statistics course, Dr. Ji Son covers information applicable for both high school and college statistics courses. She teaches through a combination of equations, diagrams, and relevant examples. Dr. Son also uses Excel to breakdown the difficult concepts of statistics into understandable and memorable ideas. Topics include everything from Central Tendency and Normal Distribution to Correlation, Probability, and Hypothesis Testing. Dr. Son has a Ph.D. in Psychology and Cognitive Science and is a published researcher on how people learn and apply abstract concepts. Excel files and data used in lessons are downloadable so students can follow along.

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Table of Contents
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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: Back-to-Back 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
			Y-axis and X-axis	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 Z-Scores		22:34
		Example 2: The Standard Normal Distribution and Z-Scores		25:54
		Example 3: Sketching and Normal Distribution		28:55
		Example 4: Sketching and Normal Distribution		32:32
	Standard Normal Distributions & Z-Scores			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
		Z-Score, Raw Score, Mean, & SD		4:08
			Z-Score, Raw Score, Mean, & SD	4:09
		Weird Z-Scores		9:40
			Weird Z-Scores	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 Z-Score 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
			Derivative-Integral 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 z-scores 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. r-squared			52:52
		Intro		0:00
		Roadmap		0:07
			Roadmap	0:08
		R-squared		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
		r-squared		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 r-squared 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 r-squared Only Range from 0 to 1		37:29
		Example 4: Find the r-squared 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
		Shape-preserving vs. Shape-changing Transformations		5:14
			Shape-preserving = Linear Transformations	5:15
			Shape-changing Transformations = Non-linear Transformations	6:20
		Common Shape-Preserving Transformations		7:08
			Common Shape-Preserving Transformations	7:09
		Common Shape-Changing Transformations		8:59
			Powers	9:00
			Logarithms	9:39
		Change Just One Variable? Both?		10:38
			Log-log 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
			Two-staged 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 Two-Stage 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, Quasi-experimental, Non-experimental	6:33
		Category III		7:42
			Experimental, Quasi-experimental, and Non-experimental	7:43
		Why CAN'T the Other Strategies Determine Causation?		10:18
			Third-variable 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
			Double-blind 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, Quasi-experimental, Non-experimental	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
		Between-subject Variable vs. Within-subject 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 (M\|L)	11:04
			P (L\|M)	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
			Non-independent Events	0:27
			Independent Events	2:00
		Non-independent and Independent Events		3:08
			Non-independent 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
			z-score and t-score: Commonality	3:03
			z-score and t-score: Formulas	3:34
			z-score and t-score: 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 t-distributions	15:14
		Rules of t Distributions		17:05
			t-distributions are More Normal as n Gets Bigger	17:06
			t-distributions 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 t-distribution' or 't-distribution'	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 p-value		45:04
			Sigma and p-value	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
		T-tests Always…		10:48
			T-tests 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 t-test: Friends on Facebook	70:1
			Two Sample t-test: 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
	F-distributions			24:46
		Intro		0:00
		Roadmap		0:04
			Roadmap	0:05
		Z- & T-statistic and Their Distribution		0:34
			Z- & T-statistic and Their Distribution	0:35
		F-statistic		4:55
			The F Ration ( the Variance Ratio)	4:56
		F-distribution		12:29
			F-distribution	12:30
		s and p-value		15:00
			s and p-value	15:01
		Example 1: Why Does F-distribution Stop At 0 But Go On Until Infinity?		18:33
		Example 2: F-distributions		19:29
		Example 3: F-distributions and Heights		21:29
	ANOVA with Independent Samples			69:25
		Intro		0:00
		Roadmap		0:05
			Roadmap	0:06
		The Limitations of t-tests		1:12
			The Limitations of t-tests	1:13
		Two Major Limitations of Many t-tests		3:26
			Two Major Limitations of Many t-tests	3:27
		Ronald Fisher's Solution… F-test! New Null Hypothesis		4:43
			Ronald Fisher's Solution… F-test! 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
			Within-group Variation	14:00
			Between-group Variation	16:22
		Time out: Review Variance & SS		17:05
			Time out: Review Variance & SS	17:06
		F-statistic		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 p-Value	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 t-tests		0:36
			Who Uploads more Pictures and Which Photo-Type is Most Frequently Used on Facebook?	0:37
		ANOVA (F-test) 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 F-statistic		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 Between-samples Variability	33:20
			Chart of Repeated Measures ANOVA: Total Variability, Within-subject (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 p-Value	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 t-tests?		73:59
XII. Chi-square Test
	Chi-Square Goodness-of-Fit Test			58:23
		Intro		0:00
		Roadmap		0:05
			Roadmap	0:06
		Where Does the Chi-Square Test Belong?		0:50
			Where Does the Chi-Square Test Belong?	0:51
		A New Twist on HT: Goodness-of-Fit		7:23
			HT in General	7:24
			Goodness-of-Fit HT	8:26
		Hypotheses about Proportions		12:17
			Null Hypothesis	12:18
			Alternative Hypothesis	13:23
			Example	14:38
		Chi-Square Statistic		17:52
			Chi-Square Statistic	17:53
		Chi-Square Distributions		24:31
			Chi-Square Distributions	24:32
		Conditions for Chi-Square		28:58
			Condition 1	28:59
			Condition 2	30:20
			Condition 3	30:32
			Condition 4	31:47
		Example 1: Chi-Square Goodness-of-Fit Test		32:23
		Example 2: Chi-Square Goodness-of-Fit Test		44:34
		Example 3: Which of These Statements Describe Properties of the Chi-Square Goodness-of-Fit Test?		56:06
	Chi-Square Test of Homogeneity			51:36
		Intro		0:00
		Roadmap		0:09
			Roadmap	0:10
		Goodness-of-Fit vs. Homogeneity		1:13
			Goodness-of-Fit 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
		Chi-Square Statistic		10:12
			Same as Goodness-of-Fit Test	10:13
		Set Up Data		12:28
			Setting Up Data Example	12:29
		Expected Frequency		16:53
			Expected Frequency	16:54
		Chi-Square Distributions & df		19:26
			Chi-Square 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: Chi-Square Test of Homogeneity		22:52
		Example 2: Chi-Square 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: Weird-MySpace-Angle Profile Photo		10:38
		Example 2: Geniuses		12:30
		Example 3: Promiscuous iPhone Users		13:37
		Example 4: Women, Aging, and Messaging		16:07

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