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This content is from OpenIntro statistics book you may be downloaded as a free PDF at https://www.openintro.org/stat/textbook.php
We hope readers will take away three ideas from this book in addition 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 learn 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 real~world.
- Introduction to data Data structures, variables, summaries, graphics, and basic data collection techniques.
- Probability (special topic) The basic principles of probability. An understanding of this chapter is not required for the main content in Chapters
- Distributions of random variables Introduction to the normal model and other key distributions.
- Foundations for inference General ideas for statistical inference in the context of estimating the population mean.
- Inference for numerical data Inference for one or two sample means using the, and also comparisons of many means using ANOVA.
- Inference for categorical data Inference for proportions using the normal and chi-square distributions, as well as simulation and randomization techniques.
- Introduction to linear regression An introduction to regression with two variables.
- Multiple and logistic regression A light introduction to multiple regression and logistic regression for an accelerated course.
OpenIntro Statistics, Second Edition is available at http://www.openintro.org under a Creative Commons Attribution-ShareAlike 3.0 Unported license (CC BY-SA):