Expected Value & Variance of Probability Distributions, Statistics

Expected Value & Variance of Probability Distributions

VIII. Probability Distributions : Lecture 2 | 53:41 min

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Expected Value & Variance of Probability Distributions

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I. Introduction
	Descriptive Statistics vs. Inferential Statistics	25:31
II. About Samples: Cases, Variables, Measurements
	About Samples: Cases, Variables, Measurements	32:14
III. Visualizing Distributions
	Introduction to Excel	8:09
	Frequency Distributions in Excel	39:10
	Frequency Distributions and Features	25:29
	Dotplots and Histograms in Excel	42:42
	Stemplots	12:23
	Bar Graphs	22:49
IV. Summarizing Distributions
	Central Tendency: Mean, Median, Mode	38:50
	Variability	42:40
	Five Number Summary & Boxplots	57:15
	Shape: Calculating Skewness & Kurtosis	41:51
	Normal Distribution	34:33
	Standard Normal Distributions & Z-Scores	41:44
	Normal Distribution: PDF vs. CDF	55:44
V. Linear Regression
	Scatterplots	47:19
	Regression	32:02
	Least Squares Regression	56:36
	Correlation	43:58
	Correlation: r vs. r-squared	52:52
	Transformations of Data	27:08
VI. Collecting Data in an Experiment
	Sampling & Bias	54:44
	Sampling Methods	14:25
	Research Design	53:54
	Between and Within Treatment Variability	41:31
VII. Review of Probability Axioms
	Sample Spaces	37:52
	Addition Rule for Disjoint Events	20:29
	Conditional Probability	57:19
	Independent Events	24:27
VIII. Probability Distributions
	Introduction to Probability Distributions	56:45
	Expected Value & Variance of Probability Distributions	53:41
	Binomial Distribution	55:15
IX. Sampling Distributions of Statistics
	Introduction to Sampling Distributions	48:17
	Sampling Distribution of the Mean	68:48
	Sampling Distribution of Sample Proportions	54:37
X. Inferential Statistics
	Introduction to Confidence Intervals	42:53
	t Distributions	62:06
	Introduction to Hypothesis Testing	66:33
	Confidence Intervals for the Difference of Two Independent Means	55:14
	Hypothesis Testing for the Difference of Two Independent Means	50:00
	Confidence Intervals & Hypothesis Testing for the Difference of Two Paired Means	74:11
	Type I and Type II Errors	31:27
	Effect Size & Power	44:41
XI. Analysis of Variance
	F-distributions	24:46
	ANOVA with Independent Samples	69:25
	Repeated Measures ANOVA	75:13
XII. Chi-square Test
	Chi-Square Goodness-of-Fit Test	58:23
	Chi-Square Test of Homogeneity	51:36
XIII. Overview of Statistics
	Overview of Statistics	18:11

Mathematics: General Statistics

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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Expected Value & Variance of Probability Distributions

Mathematics: General Statistics

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