Analyzing Data

Chapter 1: The Confidence Game: Estimation[

• Understanding Sampling Distributions
• An EXTREMELY Important Idea: The Central Limit Theorem
• Confidence: It Has Its Limits!
• Fit to a t

Chapter 2: One-Sample Hypothesis Testing

• Hypotheses, Tests, and Errors
• Hypothesis Tests and Sampling Distributions
• Catching Some Z’s Again
• Z Testing in R
• t for One
• t Testing in R
• Working with t-Distributions
• Visualizing t-Distributions
• Testing a Variance
• Working with Chi-Square Distributions
• Visualizing Chi-Square Distributions

Chapter 3: Two-Sample Hypothesis Testing

• Hypotheses Built for Two
• Sampling Distributions Revisited
• t for Two
• Like Peas in a Pod: Equal Variances
• t-Testing in R
• A Matched Set: Hypothesis Testing for Paired Samples
• Paired Sample t-testing in R
• Testing Two Variances
• Working with F Distributions
• Visualizing F Distributions

Chapter 4: Testing More than Two Samples

• Testing More than Two
• ANOVA in R
• Another Kind of Hypothesis, Another Kind of Test
• Getting Trendy
• Trend Analysis in R

Chapter 5: More Complicated Testing

• Cracking the Combinations
• Two-Way ANOVA in R
• Two Kinds of Variables … at Once
• After the Analysis
• Multivariate Analysis of Variance

Chapter 6: Regression: Linear, Multiple, and the General Linear Model

• The Plot of Scatter
• Graphing Lines
• Regression: What a Line!
• Linear Regression in R
• Juggling Many Relationships at Once: Multiple Regression
• ANOVA: Another Look
• Analysis of Covariance: The Final Component of the GLM
• But Wait — There’s More

Chapter 7: Correlation: The Rise and Fall of Relationships

• Understanding Correlation
• Correlation and Regression
• Testing Hypotheses about Correlation
• Correlation in R
• Multiple Correlation
• Partial Correlation
• Partial Correlation in R
• Semipartial Correlation
• Semipartial Correlation in R

Chapter 8: Curvilinear Regression: When Relationships Get Complicated

• What Is a Logarithm?
• What Is e?
• Power Regression
• Exponential Regression
• Logarithmic Regression
• Polynomial Regression: A Higher Power
• Which Model Should You Use?

Chapter 9: In Due Time

• A Time Series and Its Components
• Forecasting: A Moving Experience
• Forecasting: Another Way
• Working with Real Data

Chapter 10: Non-Parametric Statistics

• Independent Samples
• Matched Samples
• Correlation: Spearman’s rS
• Correlation: Kendall’s Tau
• A Heads-Up

Chapter 11: Introducing Probability

• What Is Probability?
• Compound Events
• Conditional Probability
• Large Sample Spaces
• R Functions for Counting Rules
• Random Variables: Discrete and Continuous
• Probability Distributions and Density Functions
• The Binomial Distribution
• The Binomial and Negative Binomial in R
• Hypothesis Testing with the Binomial Distribution
• More on Hypothesis Testing: R versus Tradition

Chapter 12: Probability Meets Regression: Logistic Regression

• Getting the Data
• Doing the Analysis
• Visualizing the Results