OpenIntroStatistics Fourth Edition David Diez Data Scienfist Openfntro Mine Cetinkaya-Rundel Associate Professor of the Pracfice Duke UI/niversity Professional Educator RStudio Christopher D Barr Investment Analyst Varadero Capital
Copyright @ 2019. Fourth Edition. Updated: April 12th 2022. This book may be downloaded as a free PDF at openintro.org/os. This textbook is also available under a Creative Comons license with the source files hosted on Github.
Table of Contents 1 Introduction to data 7 1.1 Case study: using stents to prevent strokes 9 1.2Data basics . . . . . 12 1.3 Sampling principles and strategies 22 1.4 Experiments . . 32 2 Summarizing data 2.1 Examining numerical data . 41 2.2 Considering categorical data . 61 2.3 Case study: malaria vaccine 71 3 Probability 79 3.1 Defining probability 81 3.2 Conditional probability 3.3 Sampling from a small population 95 112 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 4.4 Negative binomial distribution 4.3 Binomial distribution . 149 158 4.5 Poisson distribution 163 5 Foundations for inference 168 5.1 Point estimates and sampling variability 170 5.2 Confidence 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 Difference of two proportions 217 6.3 Testing for goodness of fit using chi-square . 229 6.4Testing for independence in two-way tables 240 7 Inference for numerical data 249 7.1 One-sample means with the tdistribution 251 7.2 Paired data 262 7.3 Difference of two means 267 7.4 Power calculations for a difference of means 278 7.5 Comparing many means with ANOVA 285
TABLE OF CONTENTS 8 Introduction to linear regression 8.1Fitting 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 9Multiple and logistic regression 9.1 Introduction to multiple regression 341 343 9.2Model selection . . . . . . . 353 9.3 Checking model conditions using graphs 9.4 Multiple regression case study: Mario Kart 358 365 9.5 Introcuction to logistic regression 371 A Exercise solutions B Data sets within the text 403 C Distribution tables 408
Preface 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 level in 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 foundation of 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 strengths and 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 estimating the population proportion. 6. Inference for categorical data. Inference for proportions and tables using the normal and 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 predictor variable. 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 reach multiple 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- tical summaries that are used throughout the book. ● Section 4.1 for a solid understanding of the normal distribution. ● 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.
OpenIntro StatistICS Fourth Edition(英文版).pdf
