Video tutorials to accompany Statistics: Informed Decisions Using Data, by Michael Sullivan III

Chapter 1: Data Collection

 1.1: Introduction to the Practice of Statistics  
 Example 1: Effectiveness of a Drug Treatment on the Common Cold
 Example 2: Distinguishing between Qualitative and Quantitative Variables
 Example 3: Distinguishing between Discrete and Continuous Variable
 Example 4: Distinguishing between Variables and Data
 

 1.2: Observational Studies; Simple Random Sampling  
 Example 1: Observational Studies versus Designed Experiment
 Example 2: Illustrating Simple Random Sampling
 Example 3: Obtaining a Simple Random Sample
 Example 4: Using a Graphing Calculator to Generate a Simple Random Sample
 

 1.3: Other Types of Sampling  
 Example 1: Obtaining a Stratified Sample
 Example 2: Obtaining a Systematic Sample without a Frame
 Example 3: Obtaining a Cluster Sample
 

 1.4: Sources of Error in Sampling  
 Example 1: Sources of Error in Sampling
 

 1.5: The Design of Experiments  
 Example 1: Design an Experiment
 Example 2: Illustrating the Randomized Block Design
 Example 3: A Matched-Pairs Design
 

Chapter 2: Organizing and Summarizing Data

 2.1: Organizing Qualitative Data  
 Example 1: Organizing Qualitative Data into a Frequency Distribution
 Example 2: Constructing a Relative Frequency Distribution of Qualitative Data
 Example 3: Constructing a Frequency and Relative Frequency Bar Graph
 Example 4: Comparing Two Data Sets
 Example 5: Constructing a Pie Chart
 

 2.2: Organizing Quantitative Data, I  
 Example 1: Constructing Frequency and Relative Frequency Distributions from Discrete Data
 Example 2: Drawing a Histogram for Discrete Data
 Example 3: Organizing Continuous Data into a Frequency and Relative Frequency Distribution
 Example 4: Histograms of Continuous Data
 Example 5: Stem-and-Leaf Plots
 Example 6: Constructing a Stem-and-Leaf Plot
 Example 7: Identifying the Shape of a Distribution
 

 2.3: Organizing Quantitative Data , II   
 Example 1: A Time Series Plot
 

 2.4: Graphical Misrepresentations of Data  
 Example 1: Misrepresentation of Data
 Example 2: Misrepresentation of Data by the Vertical Scale
 Example 3: Misleading Graphs
 

Chapter 3: Numerically Summarizing Data

 3.1: Measures of Central Tendency  
 Example 1: Computing a Population Mean and Sample Mean
 Example 2: Computing the Median of a Data Set with an Even Number of Observations
 Example 3: Computing the Median of a Data Set with an Odd Number of Observations
 Example 4: Finding the Mode of Quantitative Data
 Example 5: Finding the Mode of Quantitative Data
 Example 6: Determining the Mode of Quantitative Data
 Example 7: Using the Mean and the Median to Identify Distribution Shape
 Example 8: Comparing the Mean and the Median
 

 3.2: Measures of Dispersion  
 Example 1: Comparing Two Sets of Data
 Example 2: Determining the Range of a Set of Data
 Example 3: Computing a Population Variance
 Example 4: Computing a Sample Variance
 Example 5: Obtaining the Population and Sample Standard Deviation
 Example 6: Comparing the Variance and Standard Deviation of Two Data Sets
 Example 7: Using the Empirical Rule
 Example 8: Using Chebyshev's Inequality
 

 3.3: Computing Measures of Central Tendency and Dispersion from Grouped Data  
 Example 1: Approximating the Mean for Continuous Quantitative Data from the Frequency Distribution
 Example 2: Computing the Weighted Mean
 Example 3: Approximating the Variance and Standard Deviation from a Frequency Distribution
 

 3.4: Measures of Position  
 Example 1: Comparing z-scores
 Example 2: Determining the Percentile of a Data Value
 Example 3: Finding the Percentile of a Specific Data Value
 Example 4: Finding the Quartiles of a Data Set
 Example 5: Checking for Outliers
 

 3.5: The Five-Number Summary; Boxplots  
 Example 1: Obtaining the Five-Number Summary
 Example 2: Constructing a Boxplot
 Example 3: Comparing Two Distributions by Using Boxplots
 Example 4: Comparing Two Data Sets by Using Boxplots
 

Chapter 4: Describing the Relation Between Two Variables

 4.1: Scatter Diagrams; Linear Correlation  
 Example 1: Drawing a Scatter Diagram
 Example 2: Computing and Interpreting the Correlation Coefficient
 

 4.2: Least-squares Regression  
 Example 1: Finding an Equation that Describes Linearly Related Data
 Example 2: Finding the Least-Squares Regression Line
 Example 3: Comparing the Sum of Squared Residuals
 

 4.3: Diagnostics on the Least-squares Regression Line  
 Example 1: Computing the Coefficient of Determination, R2
 Example 2: Is a Linear Model Appropriate?
 Example 3: Constant Error Variance
 Example 4: Identifying Outliers
 Example 5: Graphical Residual Analysis
 Example 6: Identifying Influential Observations
 

 4.4: Nonlinear Regression: Transformations  
 Example 1: Using the Definition of the Logarithm
 Example 2: Simplifying Logarithms Using Properties (1) and (2)
 Example 3: Evaluating Exponential and Logarithmic Expressions
 Example 4: Finding the Curve of the Best Fit to an Exponential Model
 Example 5: Finding the Curve of the Best Fit to a Power Model
 

Chapter 5: Probability

 5.1: Probability of Simple Events  
 Example 1: Identifying Events and the Sample Space of a Probability Experiment
 Example 2: Computing Probabilities Using the Classical Method
 Example 3: Computing Probabilities Using Equally Likely Outcomes
 Example 4: Using Relative Frequencies to Approximate Probabilities
 Example 5: Comparing the Classical Method and Empirical Method
 Example 6: Simulating Probabilities
 

 5.2: The Addition Rule; Complements  
 Example 1: Illustrating the Addition Rule
 Example 2: Computing Probabilities Using the Addition Rule
 Example 3: Using the Addition Rule
 Example 4: Computing Probabilities Using Complements
 Example 5: Computing Probabilities Using Complements
 

 5.3: The Multiplication Rule  
 Example 1: Illustrating the Multiplication Rule
 Example 2: Computing Probabilities Using the Addition Rule
 Example 3: Using the Multiplication Rule
 Example 4: Distinguishing Independent and Dependant Events
 Example 5: Computing Probabilities of Independent Events
 Example 6: Life Expectancy
 Example 7: Sickle Cell Anemia
 Example 8: Computing "At Least" Probabilities
 

 5.4: Conditional Probability  
 Example 1: Computing a Conditional Probability
 Example 2: Birth Weights of Preterm Babies
 Example 3: Checking for Independence
 

 5.5: Counting Techniques  
 Example 1: Counting the Number of Possible Meals
 Example 2: Counting Airport Codes
 Example 3: Counting without Repetition
 Example 4: The Traveling Salesman
 Example 5: Computing Permutations
 Example 6: Betting on the Trifecta
 Example 7: Listing Combinations
 Example 8: Using Formula (2)
 Example 9: Simple Random Samples
 Example 10: Forming Different Words
 Example 11: Arranging Flags
 

Chapter 6: Discrete Probability Distributions

 6.1: Probability Distributions  
 Example 1: Distinguishing between Discrete and Continuous Random Variables
 Example 2: A Discrete Probability Distribution
 Example 3: Identifying Probability Distributions
 Example 4: Constructing a Probability Histogram
 Example 5: Computing the Mean of a Discrete Random Variable
 Example 6: Illustrating the Interpretation of the Mean of a Discrete Random Variable
 Example 7: Computing the Variance and the Standard Deviation of a Discrete Random Variance
 Example 8: Finding the Expected Value
 

 6.2: The Binomial Probability Distribution  
 Example 1: Identifying Binomial Experiments
 Example 2: Constructing a Binomial Probability Distribution
 Example 3: Using the Binomial Probability Distribution Function to Perform Inference
 Example 4: Finding the Mean and the Standard Deviation of a Binomial Random Variable
 Example 5: Constructing Binomial Probability Histograms
 Example 6: Using the Mean, Standard Deviation and Empirical Rule to Check for Unusual Results in a Binomial Experiment
 Example 7: Using the Binomial Probability Distribution Function to Perform Inference
 

 6.3: The Poisson Probability Distribution  
 Example 1: Illustrating the Poisson Process
 Example 2: Computing Probabilities of a Poisson Process
 Example 3: The Mean and Standard Deviation of a Poisson Random Variable
 Example 4: Do Beetles Follow a Poisson Probability Distribution
 Example 5: Using the Poisson Distribution to Approximate a Binomial Probability
 

Chapter 7: The Normal Probability Distribution

 7.1: Properties of the Normal Distribution  
 Example 1: Illustrating the Uniform Distribution
 Example 2: Area as a Probability
 Example 3: A Normal Random Variable
 Example 4: Interpreting the Area under the Normal Curve
 Example 5: Relation between a Normal Random Variable and a Standard Normal Random Variable
 

 7.2: The Standard Normal Distribution  
 Example 1: Finding Area under the Standard Normal Curve Left of a z-score
 Example 2: Finding Area under the Standard Normal Curve Right of a z-score
 Example 3: Finding Area under the Standard Normal Curve between Two z-scores
 Example 4: Finding a Z-score from a Specified Area to the left
 Example 5: Finding a Z-score from a Specified Area to the right
 Example 6: Finding the Z-score from the Area in the Middle
 Example 7: Finding the Value of z
 Example 8: Finding Probabilities Standard Normal Random Variables
 

 7.3: Applications of the Normal Distribution  
 Example 1: Finding Area under a Normal Probability Plot
 Example 2: Finding the Probability of a Normal Random Variable
 Example 3: Finding the Value of a Normal Random Variable
 Example 4: Finding the Value of a Normal Random Variable
 

 7.4: Assessing Normality  
 Example 1: Constructing a Normal Probability Plot
 Example 2: Assessing Normality Via Statistical Software
 Example 3: Assessing Normality
 

 7.5: Sampling Distributions; The Central Limit Theorem  
 Example 1: Illustrating a Sampling Distribution
 Example 2: Sampling Distribution of the Sampling Variability
 Example 3: The Impact of Sample Size on Sampling Variability
 Example 4: Describing the Distribution of the Sample Mean
 Example 5: Sampling from a Population That is Not Normal
 

 7.6: The Normal Approximation to the Binomial Probability Distribution  
 Example 1: The Normal Approximation to a Binomial Random Variable
 Example 2: A Normal Approximation to the Binomial
 

Chapter 8: Confidence Intervals about a Single Parameter

 8.1: Confidence Intervals about a Population Mean, known  
 Example 1: Computing a Point Estimate for µ
 Example 2: Constructing 10 - 95% Confidence Intervals Based on 20 Samples
 Example 3: Construction a z-interval
 Example 4: The Role of the Level of Confidence in the Margin of Error
 Example 5: The Role of Sample Size in the Margin of Error
 Example 6: Determining Sample Size
 

 8.2: Confidence Intervals about a Population Mean, unknown  
 Example 1: Finding t-Values
 Example 2: Constructing a Confidence Interval about µ, - unknown
 Example 3: The Effect of Outliers
 

 8.3: Confidence Intervals about the Population Proportion  
 Example 1: Obtaining a Point Estimate of a Population Proportion
 Example 2: Constructing a Confidence Interval for a Population Proportion
 Example 3: Determining Sample Size
 

 8.4: Confidence Intervals about a Population Standard Deviation  
 Example 1: Finding Critical Values for the Chi-Square Distribution
 Example 2: Constructing a Confidence Interval about a Population Variance and Standard Deviation
 

Chapter 9: Hypothesis Testing

 9.1: The Language of Hypothesis Testing  
 Example 1: Illustrating Hypothesis Testing
 Example 2: Forming Hypothesis
 Example 3: Type I and Type II Errors
 Example 4: Stating the Conclusion
 

 9.2: Testing a Hypothesis about µ, known  
 Example 1: The Classical Method of Hypothesis Testing
 Example 2: The Classical Method of Hypothesis Testing
 Example 3: Computing the p-value of a Right-Tailed Test
 Example 4: Computing the p-value of a Two-Tailed Test
 Example 5: Using a Confidence Interval to Test a Hypothesis
 

 9.3: Testing a Hypothesis about µ, unknown  
 Example 1: Caffeine intake of 20-29 Year-Old Females
 Example 2: Approximating a p-Value
 

 9.4: Testing a Hypothesis about the Population Proportion  
 Example 1: Who are Thought to Be More Aggressive, Men or Women?
 Example 2: Side Effects of Prevnar
 Example 3: Hypothesis Test for a Proportion-Small Sample Size
 

 9.5: Testing a Hypothesis about  
 Example 1: Testing a Hypothesis about a Population Standard Deviation
 Example 2: Testing a Hypothesis about a Population Standard Deviation
 Example 3: Approximating the p-Value
 

 9.6: The Probability of a Type II Error; the Power of the Test  
 Example 1: Computing the Probability of a Type II Error
 Example 2: Computing the Power of the Test
 

Chapter 10: Comparing Two Population Parameters

 10.1: Inference about Two Means: Dependent Samples  
 Example 1: Distinguishing between Independent and Dependent Sampling
 Example 2: Testing a Claim Regarding Matched-Pairs Data
 Example 3: Constructing a Confidence Interval for Matched-Pairs Data
 

 10.2: Inference about Two Means: Independent Samples  
 Example 1: Testing a Claim Regarding Two Means
 Example 2: Constructing a Confidence Interval about the Difference of Two Means
 

 10.3: Inference about Two Population Proportions  
 Example 1: Testing the Claim Regarding To Population Proportions
 Example 2: Constructing a Confidence Interval for the Difference between Two Population Proportions
 Example 3: Determining Sample Size
 

 10.4: Inference about Two Population Standard Deviations  
 Example 1: Finding Critical Values for the F-Distribution
 Example 2: Testing a Claim Regarding Two Population Standard Deviations
 Example 3: Testing a Claim Regarding Two Population Standard Deviations
 

Chapter 11: Chi-Square Procedures

 11.1: Chi-Square Goodness of Fit Test  
 Example 1: Finding Expected Counts
 Example 2: Testing a Claim Using the Goodness-of-Fit Test
 Example 3: Testing a Claim Using the Goodness-of-Fit Test
 

 11.2: Contingency Tables; Association  
 Example 1: Determining Frequency Marginal Distributions
 Example 2: Determining Relative Frequency Marginal Distributions
 Example 3: Comparing Two Categories of a Variable
 Example 4: Constructing a Conditional Distribution
 Example 5: Drawing a Bar Graph of a Conditional Distribution
 

 11.3: Chi-Square Test for Independence; Homogeneity of Proportions  
 Example 1: Determining the Expected Counts in a Test for Independence
 Example 2: Performing a Chi-Square Independence Test
 Example 3: Constructing a Conditional Distribution and Bar Graph
 Example 4: A Test for Homogeneity of Proportions
 

Chapter 12: Inference on the Least-squares Regression Model; ANOVA

 12.1: Inference about the Least-squares Regression Model  
 Example 1:Least-Squares Regression
 Example 2: Computing the Standard Error
 Example 3: Verifying that the Residuals are Normally Distributed
 Example 4: Testing for a Linear Relation
 Example 5: Constructing a Confidence Interval about the Slope of the True Regression Line
 

 12.2: Confidence and Prediction Intervals  
 Example 1: Constructing a Confidence Interval about the Mean Predicted Value
 Example 2: Constructing a Predicting Interval about a Predicted Value
 

 12.3: One-way Analysis of Variance  
 Example 1: Testing the Requirements of a One-Way ANOVA
 Example 2: Using Technology to Perform One-Way ANOVA tests
 Example 3: Computing the F Test Statistics
 

Chapter 13: Nonparametric Statistics

 13.1: An Overview of Nonparametric Statistics   

 13.2: Runs Test for Randomness  
 Example 1: Utilizing the Notation in the Runs Test for Randomness
 Example 2: Obtaining Critical Values from table IV
 Example 3: Testing for Randomness (Small-Sample Case)
 Example 4: Testing for Randomness (Large-Sample Case)
 

 13.3: Inferences about Measures of Central Tendency  
 Example 1: Conducting a One-Sample Sign Test (Small-Sample Case)
 Example 2: Conducting a One-Sample Sign Test (Large-Sample Case)
 

 13.4: Inferences about the Difference between Two Measures of Central Tendency: Dependent Samples  
 Example 1: Illustration of the Wilcoxon Matched-Pairs Signed-Ranks Test (Small-Sample Case)
 

 13.5: Inferences about the Difference between Two Measures of Central Tendency: Independent Samples  
 Example 1: Illustrating the Mann-Whitney Test (Small-Sample Case)
 Example 2: Illustrating the Mann-Whitney Test (Large-Sample Case)
 

 13.6: Spearman Rank Correlation Coefficient  
 Example 1: Illustrating Spearman's Rank-Correlation Test
 

 13.7: Kruskal-Wallis Test  
 Example 1: Illustrating the Kruskal-Wallis Test