OpenIntroStatistics Fourth Edition
David DiezData Scienfist Openfntro
Mine Cetinkaya-RundelAssociate Professor of the Pracfice Duke UI/niversity Professional Educator RStudio
Christopher D Barr Investment AnalystVaradero Capital
Copyright @ 2019. Fourth Edition. Updated: April 12th 2022. This book may be downloaded as a free PDF at /os. This textbook is also available under a Creative Comons license with the source files hosted on Github.
Table of Contents
7
1 Introduction to data
1.2Data basics . . . . . 1.1 Case study: using stents to prevent strokes 12 91.3 Sampling principles and strategies 221.4 Experiments . . 32
2 Summarizing data
2.1 Examining numerical data . 412.2 Considering categorical data . 612.3 Case study: malaria vaccine 71
79
3 Probability
3.1 Defining probability 813.3 Sampling from a small population 3.2 Conditional probability 112 953.4 Random variables 1153.5 Continuous distributions . 125
4.1 Normal distribution 1334.2 Geometric distribution . 4.3 Binomial distribution . 144 1494.4 Negative binomial distribution 1584.5 Poisson distribution 163
168
5 Foundations for inference
5.1 Point estimates and sampling variability 1705.2 Confidence intervals for a proportion 1815.3 Hypothesis testing for a proportion 189
6 Inference for categorical data 206
6.1 Inference for a single proportion . 2086.2 Difference of two proportions 6.3 Testing for goodness of fit using chi-square . 217 2296.4Testing for independence in two-way tables 240
7 Inference for numerical data 249
7.2 Paired data 7.1 One-sample means with the tdistribution 251 2627.3 Difference of two means 2677.4 Power calculations for a difference of means 7.5 Comparing many means with ANOVA 278 285
8.1Fitting a line residuals and correlation 8.2 Least squares regression . . . 305 3178.3 Types of outliers in linear regression 3288.4 Inference for linear regression 331
9.1 Introduction to multiple regression 3439.2Model selection . . . . . . . 9.3 Checking model conditions using graphs 353 3589.4 Multiple regression case study: Mario Kart 3659.5 Introcuction to logistic regression 371
A Exercise solutionsB Data sets within the text 403C Distribution tables 408
OpenIntro Statistics covers a first course in statistics providing a rigorous introduction to applied statistics that is clear concise and accessible. This book was written with the undergradluate levelin mind but it's also popular in high schools and graduate courses.
We hope readers wil take away thre ideas from this book in adition to forming a foundationof statistical thinking and methods.
● Statistics is an applied field with a wide range of practical applications.● You don’t have to be a math guru to learm from real interesting data.● Data are messy and statistical tools are imperfect. But when you understand the strengthsand weaknesses of these tools you can use them to learn about the world.
Textbook overview
The chapters of this book are as follows:
1. Introduction to data. Data structures variables and basic data collection techniques.2. Summarizing data. Data summaries graphics and a teaser of inference using randomization.3. Probability. Basic principles of probability. 4. Distributions of random variables. The normal model and other key distributions.5. Foundations for inference. General ideas for statistical inference in the context of estimating6. Inference for categorical data. Inference for proportions and tables using the normal and the population proportion.chi-square distributions.7. Inference for numerical data. Inference for one or two sample means using the tdistribution. statistical power for paring two groups and also parisons of many means using ANOVA.8. Introduction to linear regression. Regression for a numerical oute with one predictorvariable. Most of this chapter could be covered after Chapter 1.9. Multiple and logistice regression. Regression for mumerical and categorical data using many predictors.
Openntro Statistics supports flexibility in choosing and ordering topics. If the main goal is to reachmultiple regression (Chapter 9) as quickly as possible then the following are the ideal prerequisites:
● Chapter 1 Sections 2.1 and Section 2.2 for a solid introduction to data structures and statis-● Section 4.1 for a solid understanding of the normal distribution. tical summaries that are used throughout the book.● Chapter 5 to establish the core set of inference tools.● Section 7.1 to give a foundation for the t-distribution● Chapter 8 for establishing ideas and principles for single predictor regresion.
