1CHAPTER 1 Introduction
27CHAPTER 6 Classification
21.1 What is Statistics?
286.1 Binary Classification
31.2 Probability
296.2 Logistic Regression
41.3 Why Study Statistics?
306.3 Decision Trees
5CHAPTER 2 Descriptive Statistics
31CHAPTER 7 Resampling Methods
62.1 Summarizing Data
327.1 Bootstrapping
72.2 Measures of Center
337.2 Cross-Validation
82.3 Measures of Spread
34CHAPTER 8 Regression
92.4 Exploratory Data Analysis
358.1 Clustering
10CHAPTER 3 Probability
368.2 Principal Component Analysis
113.1 Basic Probability
37CHAPTER 9 Causal Inference
123.2 Conditional Probability
389.1 Observational Studies
133.3 Bayes’ Rule
399.2 Randomized Experiments
143.4 Random Variables
409.3 Causal Inference Methods
153.5 Probability Distributions
41CHAPTER 10 Bayesian Statistics
16CHAPTER 4 Statistical Inference
4210.1 Bayesian Inference
174.1 Point Estimation
4310.2 Markov Chain Monte Carlo (MCMC)
184.2 Interval Estimation
4410.3 Bayesian Networks
194.3 Hypothesis Testing
45CHAPTER 11 Statistical Computing
204.4 Significance Testing
4611.1 Computational Tools
214.5 Confidence Intervals
4711.2 R and Python for Statistics
224.6 Sampling Distributions
48CHAPTER 12 Future Directions
23CHAPTER 5 Regression
4912.1 Further Reading
245.1 Simple Linear Regression
5012.2 Exercises and Solutions
255.2 Multiple Linear Regression
51Glossaries
265.3 Generalized Linear Models
52Index
