Contents
1.3.2 Representing Missing Data in R
1.3.3 Vectors and Vectorization in R
1.3.4 A Brief Introduction to Matrices
1.3.6 A Quick Introduction to Data Frames
Chapter 2: Dealing with Dates, Strings, and Data Frames
2.1 Working with Dates and Times
2.3 Working with Data Frames in the Real World
2.3.1 Finding and Subsetting Data
2.4 Manipulating Data Structures
2.5 The Hard Work of Working with Larger Datasets
3.1.3 Other Ways to Get Data Into R
3.1.4 Reading Data from a File
3.1.5 Getting Data from the Web
4.3 If, If-Else, and ifelse() Statements
Chapter 5: Functional Programming
5.2 Reserved Names and Syntactically Correct Names
5.4.2 A Function with Arguments
5.5.1 S3 Class and Method Example
5.5.2 S3 Methods for Existing Classes
Chapter 6: Probability Distributions
6.1 Discrete Probability Distributions
6.2.1 The Poisson Distribution
6.2.2 Some Other Discrete Distributions
6.3 Continuous Probability Distributions
6.3.4 The Chi-Square Distribution
Chapter 7: Working with Tables
7.1 Working with One-Way Tables
7.2 Working with Two-Way Tables
Chapter 8: Descriptive Statistics and Exploratory Data Analysis
8.2.2 The Variance and Standard Deviation
8.3 Boxplots and Stem-and-Leaf Displays
8.4 Using the fBasics Package for Summary Statistics
Chapter 9: Working with Graphics
9.1 Creating Effective Graphics
9.2 Graphing Nominal and Ordinal Data
9.3.3 Frequency Polygons and Smoothed Density Plots
Chapter 10: Traditional Statistical Methods
10.1 Estimation and Confidence Intervals
10.1.1 Confidence Intervals for Means
10.1.2 Confidence Intervals for Proportions
10.1.3 Confidence Intervals for the Variance
10.2 Hypothesis Tests with One Sample
10.3 Hypothesis Tests with Two Samples
Chapter 11: Modern Statistical Methods
11.1 The Need for Modern Statistical Methods
11.2 A Modern Alternative to the Traditional t Test
Chapter 12: Analysis of Variance
12.3.1 Repeated-Measures ANOVA
> results <- aov ( fitness ~ time + Error (id / time ), data = repeated)
Chapter 13: Correlation and Regression
13.1 Covariance and Correlation
13.2 Linear Regression: Bivariate Case
13.3 An Extended Regression Example: Stock Screener
13.3.1 Quadratic Model: Stock Screener
13.4 Confidence and Prediction Intervals
Chapter 14: Multiple Regression
14.1 The Conceptual Statistics of Multiple Regression
14.2 GSS Multiple Regression Example
14.2.1 Exploratory Data Analysis
14.2.2 Linear Model (the First)
14.2.3 Adding the Next Predictor
Chapter 15: Logistic Regression
15.1 The Mathematics of Logistic Regression
15.2 Generalized Linear Models
15.3 An Example of Logistic Regression
15.3.1 What If We Tried a Linear Model on Age?
15.3.2 Seeing If Age Might Be Relevant with Chi Square
15.3.3 Fitting a Logistic Regression Model
15.3.4 The Mathematics of Linear Scaling of Data
15.3.5 Logit Model with Rescaled Predictor
15.3.6 Multivariate Logistic Regression
15.4 Ordered Logistic Regression
15.4.1 Parallel Ordered Logistic Regression
15.4.2 Non-Parallel Ordered Logistic Regression
Chapter 16: Modern Statistical Methods II
16.2.1 Wilcoxon-Signed-Rank Test
16.3.2 Bootstrapping Confidence Intervals
Chapter 17: Data Visualization Cookbook
17.3 Customizing and Polishing Plots
Chapter 18: High-Performance Computing
18.2.1 Other Parallel Processing Approaches
19.1 Installing Needed Packages and Software