Introductory Statistics

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Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs. Access also available here: https://openstax.org/details/books/introductory-statistics Table of Contents Sampling and Data Descriptive Statistics Probability Topics Discrete Random Variables Continuous Random Variables The Normal Distribution The Central Limit Theorem Confidence Intervals Hypothesis Testing with One Sample Hypothesis Testing with Two Samples The Chi-Square Distribution Linear Regression and Correlation F Distribution and One-Way ANOVA

Material Type: Textbook

Authors: Barbara Ilowsky, Susan Dean

Introductory Statistics with Randomization and Simulation First Edition

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We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods. (1) Statistics is an applied field with a wide range of practical applications. (2) You don't have to be a math guru to learn from interesting, real data. (3) Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world. Reviews available here: https://open.umn.edu/opentextbooks/textbooks/introductory-statistics-with-randomization-and-simulation-first-edition

Material Type: Textbook

Authors: Christopher Barr, David Diez, Mine Çetinkaya-Runde

Statistical Thinking for the 21st Century

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Statistical thinking is a way of understanding a complex world by describing it in relatively simple terms that nonetheless capture essential aspects of its structure, and that also provide us some idea of how uncertain we are about our knowledge. The foundations of statistical thinking come primarily from mathematics and statistics, but also from computer science, psychology, and other fields of study. Table of Contents 1 Introduction 2 Working with data 3 Probability 4 Summarizing data 5 Fitting models to data 6 Data Visualization 7 Sampling 8 Resampling and simulation 9 Hypothesis testing 10 Confidence intervals, effect sizes, and statistical power 11 Bayesian statistics 12 Modeling categorical relationships 13 Modeling continuous relationships 14 The General Linear Model 15 Comparing means 16 The process of statistical modeling: A practical example 17 Doing reproducible research

Material Type: Textbook

Author: Russell A. Poldrack