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Getting Started (Don't Skip This Part)
College / Accelerated Statistics with R (XCD)
PART I: EXPLORING AND MODELING VARIATION
Chapter 1 - Exploring Data with R
- 1.1 Welcome to Statistics and Data Science
- 1.2 Getting Started with R
- 1.3 Introduction to R Functions
- 1.4 Save Your Work in R Objects
- 1.5 Working With Data Frames in R
- 1.6 Variable Types in R
- 1.7 Selecting and Filtering Data in R
- 1.8 Missing Data
- 1.9 Recoding and Creating Variables
- 1.10 Visualizing and Summarizing Quantitative Variables
- 1.11 Visualizing and Summarizing Categorical Variables
Chapter 2 - From Exploring to Modeling Variation
- 2.1 Relationships between two variables
- 2.2 Visualizing Two-Variable Relationships
- 2.3 More Two-Variable Visualizations
- 2.4 Explaining Variation in an Outcome Variable
- 2.5 The Mean as a Model
- 2.6 Fitting the Empty Model
- 2.7 DATA = MODEL + ERROR: Notation
- 2.8 Parameters and Estimates
- 2.9 Quantifying Total Error Around a Model
- 2.10 The Beauty of Sum of Squares
Chapter 3 - Modeling Relationships in Data
- 3.1 Explaining Variation
- 3.2 Using R to Fit the `Neighborhood` Model
- 3.3 GLM Notation for the `Neighborhood` Model
- 3.4 Error from the `Neighborhood` Model
- 3.5 Error Reduced by the Neighborhood Model
- 3.6 Using a Quantitative Explanatory Variable in a Model
- 3.7 Specifying the `HomeSizeK` Model with GLM Notation
- 3.8 Interpreting the Parameter Estimates for a Regression Model
- 3.9 Error from the `HomeSizeK` Model
- 3.10 Using ANOVA Tables to Compare Models
- 3.11 Conceptualizing SS Model
- 3.12 Comparing Models with PRE
- 3.13 Comparing Models with F
PART II: COMPARING MODELS TO MAKE INFERENCES
Chapter 4 - The Logic of Inference
- 4.1 The Problem of Inference
- 4.2 Constructing a Sampling Distribution
- 4.3 Exploring the Sampling Distribution of b1
- 4.4 What Counts as Unlikely
- 4.5 The p-Value
- 4.6 Calculating the p-Value for a Sample
- 4.7 A Mathematical Model of the Sampling Distribution of b1
- 4.8 Things That Affect p-Value
- 4.9 Hypothesis Testing for Regression Models
- 4.10 Chapter 4 Review Questions
- 4.11 Chapter 4 Review Questions 2
Chapter 5 - Model Comparison with F
- 5.1 Moving Beyond b1
- 5.2 Sampling Distribution of PRE
- 5.3 Sampling Distribution of F
- 5.4 Using the Sampling Distribution of F
- 5.5 Calculating P-Value from the Sampling Distribution of F
- 5.6 The F-Distribution: A Mathematical Model of the Sampling Distribution of F
- 5.7 F-Distribution and t-Distribution
- 5.8 Using F to Test a Regression Model
- 5.9 Type I and Type II Error
- 5.10 Using F to Compare Multiple Groups
- 5.11 Pairwise Comparisons
- 5.12 The Problem of Simultaneous Comparisons
- 5.13 The Chi-Square Test of Independence
- 5.14 Chapter 5 Review Questions
- 5.15 Chapter 5 Review Questions 2
Chapter 6 - Parameter Estimation and Confidence Intervals
- 6.1 From Hypothesis Testing to Confidence Intervals
- 6.2 Thinking With Sampling Distributions
- 6.3 The Basic Idea Behind Confidence Intervals
- 6.4 Using Bootstrapping to Calculate the 95% Confidence Interval
- 6.5 Using the Bootstrapped Sampling Distribution to Find the Confidence Interval
- 6.6 Shuffle, Resample, and Standard Error
- 6.7 Using the t-Distribution to Construct a Confidence Interval
- 6.8 Interpreting the Confidence Interval
- 6.9 Confidence Intervals and Model Comparison
- 6.10 Confidence Interval for Beta0
- 6.11 Confidence Interval for the Slope of a Regression Line
- 6.12 Confidence Intervals for Pairwise Comparisons
- 6.13 What Affects the Width of the Confidence Interval
- 6.14 Chapter 6 Review Questions
- 6.15 Chapter 6 Review Questions 2
PART III: MULTIVARIATE MODELS
Chapter 7 - Introduction to Multivariate Models
- 7.1 Models with Two Explanatory Variables
- 7.2 Visualizing Price = Home Size + Neighborhood
- 7.3 Specifying and Fitting a Multivariate Model
- 7.4 Interpreting the Parameter Estimates for a Multivariate Model
- 7.5 Predictions from the Multivariate Model
- 7.6 Using Residuals and Sums of Squares to Measure Error Around the Multivariate Model
- 7.7 Using Venn Diagrams to Conceptualize Sums of Squares, PRE, and F
- 7.8 The Logic of Inference with the Multivariate Model
- 7.9 Using the Sampling Distribution of F
Chapter 8 - Multivariate Model Comparisons
- 8.1 Targeted Model Comparisons
- 8.2 Sums of Squares for Targeted Model Comparisons
- 8.3 PRE and F for Targeted Model Comparisons
- 8.4 Inference for Targeted Model Comparisons
- 8.5 Using `shuffle()` for Targeted Model Comparisons (Part 1)
- 8.6 Using `shuffle()` for Targeted Model Comparisons (Part 2)
- 8.7 Deciding Which Predictors to Include in a Model
- 8.8 Models with Multiple Categorical Predictors
- 8.9 Error and Inference from Models with Multiple Categorical Predictors
- 8.10 Models with Multiple Quantitative Predictors
- 8.11 Error and Inference from Models with Multiple Quantitative Predictors
Chapter 9 - Models with Interactions
- 9.1 Dogs in the Emergency Room
- 9.2 Additive versus Non-Additive Models
- 9.3 Representing the Interaction Model in GLM Notation
- 9.4 Interpreting Parameter Estimates for the Interaction Model
- 9.5 Making Parameter Estimates More Meaningful in Interaction
- 9.6 Centering a Quantitative Predictor at 0
- 9.7 Comparing the Interaction Model to the Additive Model (Part 1)
- 9.8 Comparing the Interaction Model to the Additive Model (Part 2)
Chapter 10 - More Models with Interactions
- 10.1 Interactions with Two Quantitative Predictors
- 10.2 Fitting and Visualizing an Interaction Model with Two Quantitative Predictors
- 10.3 Interpreting Parameter Estimates of Interaction Models with Two Quantitative Predictors
- 10.4 Comparing the Interaction Model to the Additive Model with Two Quantitative Predictors
- 10.5 Interactions with Two Categorical Predictors
- 10.6 Predictions of the Interaction Model with Two Categorical Predictors
- 10.7 Visually Comparing the Interaction Model to the Additive Model with Two Categorical Predictors
- 10.8 Thinking of Factorial Models in Terms of Intercepts and Slopes