OpenIntro Statistics - Fourth Edition - Learning statistical thinking and applied inference

Sep 14, 2026

Uploaded by Theodore

OpenIntro Statistics, Fourth Edition, provides a clear and accessible first course in applied statistics. It builds statistical thinking through practical examples and a structured progression from data basics and sampling to probability, distributions, and core inference. Readers learn to summarize numerical and categorical data, interpret confidence intervals and perform hypothesis tests, and handle inference for categorical and numerical outcomes. The book culminates with linear, multiple, and logistic regression, supported by exercises and reference materials.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages422

Reading Time17 h 43 min

AccessFree to download

Table of Contents

1 Introduction to data

2 Summarizing data

3 Probability

4 Distributions of random variables

5 Foundations for inference

6 Inference for categorical data

7 Inference for numerical data

8 Introduction to linear regression

9 Multiple and logistic regression

A Exercise solutions

B Data sets within the text

C Distribution tables

OpenIntro Statistics Fourth Edition David Diez Data Scientist OpenIntro Mine C􀀘 etinkaya-Rundel Associate Professor of the Practice, Duke University Professional Educator, RStudio Christopher D Barr Investment Analyst Varadero Capital Copyright © 2019. Fourth Edition. Updated: August 12th, 2019 . This book may be downloaded as a free PDF at [openintro.org/os](openintro.org/os. This)[. This](openintro.org/os. This) textbook is also available under a Creative Commons license, with the source 􀀌les hosted on Github. 3 Table of Contents 1 Introduction to data 7 1.1 Case study: using stents to prevent strokes ....................... 9 1.2 Data basics ......................................... 12 1.3 Sampling principles and strategies ............................ 22 1.4 Experiments ......................................... 32 2 Summarizing data 39 2.1 Examining numerical data ................................. 41 2.2 Considering categorical data ................................ 61 2.3 Case study: malaria vaccine ................................ 71 3 Probability 79 3.1 De􀀌ning probability .................................... 81 3.2 Conditional probability .................................. 95 3.3 Sampling from a small population ............................ 112 3.4 Random variables ...................................... 115 3.5 Continuous distributions .................................. 125 4 Distributions of random variables 131 4.1 Normal distribution .................................... 133 4.2 Geometric distribution ................................... 144 4.3 Binomial distribution .................................... 149 4.4 Negative binomial distribution .............................. 158 4.5 Poisson distribution .................................... 163 5 Foundations for inference 168 5.1 Point estimates and sampling variability ......................... 170 5.2 Con􀀌dence intervals for a proportion ........................... 181 5.3 Hypothesis testing for a proportion ............................ 189 6 Inference for categorical data 206 6.1 Inference for a single proportion .............................. 208 6.2 Di􀀋erence of two proportions ............................... 217 6.3 Testing for goodness of 􀀌t using chi-square ........................ 229 6.4 Testing for independence in two-way tables ....................... 240 7 Inference for numerical data 249 7.1 One-sample means with the t-distribution ........................ 251 7.2 Paired data ......................................... 262 7.3 Di􀀋erence of two means .................................. 267 7.4 Power calculations for a di􀀋erence of means ....................... 278 7.5 Comparing many means with ANOVA .......................... 285 4 TABLE OF CONTENTS 8 Introduction to linear regression 303 8.1 Fitting a line, residuals, and correlation ......................... 305 8.2 Least squares regression .................................. 317 8.3 Types of outliers in linear regression ........................... 328 8.4 Inference for linear regression ............................... 331 9 Multiple and logistic regression 341 9.1 Introduction to multiple regression ............................ 343 9.2 Model selection ....................................... 353 9.3 Checking model conditions using graphs ......................... 358 9.4 Multiple regression case study: Mario Kart ....................... 365 9.5 Introduction to logistic regression ............................. 371 A Exercise solutions 384 B Data sets within the text 403 C Distribution tables 408 5 Preface OpenIntro Statistics covers a 􀀌rst course in statistics, providing a rigorous introduction to applied statistics that is clear, concise, and accessible. This book was written with the undergraduate level in mind, but it's also popular in high schools and graduate courses. We ho

FAQs

What topics does OpenIntro Statistics cover in this edition?

It covers data introduction and sampling, data summarization, probability, key distributions, foundations for inference, inference for categorical and numerical data, and regression including linear, multiple, and logistic regression.

How does the book approach learning statistics for real-world use?

It emphasizes applied statistics with real, interesting data and highlights both strengths and weaknesses of statistical tools, teaching readers to interpret imperfect methods responsibly.

What prerequisites does the book suggest for reaching multiple regression quickly?

It recommends completing core sections for data structures and summaries, understanding the normal distribution, and covering Chapter 5 concepts before moving toward multiple regression in Chapter 9.

Sep 14, 2026

Uploaded by Theodore