Black and white paperback edition, with this new edition released in May 2019.
The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. Our inaugural effort is OpenIntro Statistics. Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org, and anybody can purchase a paperback copy from amazon.com for about $20.
OpenIntro has grown through the involvement and enthusiasm of our community. Visit our website, openintro.org. We provide videos, labs for R and SAS, teaching resources like slides, and many other helpful resources.
Author(s): David Diez, Mine Çetinkaya-Rundel, Christopher Barr
Edition: 4
Publisher: OpenIntro, Inc.
Year: 2019
Language: English
Pages: 422
Tags: Statistics
Title Page
Copyright Page
Table of Contents
Preface
1 Introduction to data
1.1 Case study: using stents to prevent strokes
1.2 Data basics
1.3 Sampling principles and strategies
1.4 Experiments
2 Summarizing data
2.1 Examining numerical data
2.2 Considering categorical data
2.3 Case study: malaria vaccine
3 Probability
3.1 Defining probability
3.2 Conditional probability
3.3 Sampling from a small population
3.4 Random variables
3.5 Continuous distributions
4 Distributions of random variables
4.1 Normal distribution
4.2 Geometric distribution
4.3 Binomial distribution
4.4 Negative binomial distribution
4.5 Poisson distribution
5 Foundations for inference
5.1 Point estimates and sampling variability
5.2 Confidence intervals for a proportion
5.3 Hypothesis testing for a proportion
6 Inference for categorical data
6.1 Inference for a single proportion
6.2 Difference of two proportions
6.3 Testing for goodness of fit using chi-square
6.4 Testing for independence in two-way tables
7 Inference for numerical data
7.1 One-sample means with the t-distribution
7.2 Paired data
7.3 Difference of two means
7.4 Power calculations for a difference of means
7.5 Comparing many means with ANOVA
8 Introduction to linear regression
8.1 Fitting a line, residuals, and correlation
8.2 Least squares regression
8.3 Types of outliers in linear regression
8.4 Inference for linear regression
9 Multiple and logistic regression
9.1 Introduction to multiple regression
9.2 Model selection
9.3 Checking model conditions using graphs
9.4 Multiple regression case study: Mario Kart
9.5 Introduction to logistic regression
A Exercise solutions
B Data sets within the text
C Distribution tables
