This is an example. When a question is asked here, where can the answer be found?
Solution.
The answer can be found here, in the solution section of the example!
Preface Preface
This book 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 undergraduate level in mind, but it’s also popular in high schools and graduate courses.
We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.
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Statistics is an applied field with a wide range of practical applications.
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You don’t have to be a math guru to learn from real, interesting data.
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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:
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Introduction to data 1 Data structures, variables, and basic data collection techniques.
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Summarizing data 2 Data summaries, graphics, and a teaser of inference using randomization.
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Probability 3 Basic principles of probability.
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Distributions of random variables 4 The normal model and other key distributions.
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Foundations for inference 5 General ideas for statistical inference in the context of estimating the population proportion.
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Inference for categorical data 6 Inference for proportions and tables using the normal and chi-square distributions.
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Inference for numerical data 7 Inference for one or two sample means using the t-distribution, statistical power for comparing two groups, and also comparisons of many means using ANOVA.
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Introduction to linear regression 8 Regression for a numerical outcome with one predictor variable. Most of this chapter could be covered after Chapter 1.
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Multiple and logistic regression 9 Regression for numerical and categorical data using many predictors.
Introductory 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:
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Chapter 1, Section 2.1, and Section 2.2 for a solid introduction to data structures and statistical summaries that are used throughout the book.
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Section 4.1 for a solid understanding of the normal distribution.
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Chapter 5 to establish the core set of inference tools.
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Section 7.1 to give a foundation for the t-distribution.
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Chapter 8 for establishing ideas and principles for single predictor regression.
Examples and Exercises.
Examples are provided to establish an understanding of how to apply methods.
Example 0.0.1.
When we think the reader should be ready to try determining the solution to an example, we frame it as Guided Practice.
Checkpoint 0.0.2.
The reader may check or learn the answer to any Guided Practice problem by reviewing the full solution in a footnote.
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Guided Practice problems are intended to stretch your thinking, and you can check yourself by reviewing the footnote solution for any Guided Practice.
Exercises are also provided at the end of each section as well as review exercises at the end of each chapter. Solutions are given for odd-numbered exercises in Appendix A.
Additional Resources.
This is a PreTeXt adaptation of OpenIntro Statistics (Fourth Edition) by David Diez, Mine Çetinkaya-Rundel, and Christopher Barr. This adaptation is independent and is not affiliated with or endorsed by OpenIntro.
The original authors have made a range of supporting materials available for the original textbook, including video overviews, slides, statistical software labs, and data sets. These may be found at openintro.org/os, though note that these resources correspond to the original book and may not align precisely with this PreTeXt version.
Data sets used in the original text are described in Appendix B. Online guides to these data sets are also available at openintro.org/data and through a companion R package, though again these are maintained by OpenIntro independently of this adaptation.
If you find any errors or typos in this PreTeXt adaptation specifically, please report them by opening an issue on the GitHub repository.
Readers interested in a high school level introduction to statistics may wish to explore Advanced High School Statistics, an adaptation of the original OpenIntro Statistics textbook customised by Leah Dorazio for high school courses and AP® Statistics. More information is available at openintro.org.
