Digital library
Textbooks, references and open learning resources.
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Introduction to Applied Statistics for Psychology Students
Gordon E. Sarty · University of Saskatchewan
In the contents: 02: Descriptive Statistics - Frequency Data (Counting) · 03: Descriptive Statistics- Central Tendency and Dispersion · 6.04: Robust Statistics
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Applied Statistics for Social Science (19-20)
LibreTexts
In the contents: 1.2: Definitions of Statistics Probability and Key Terms · 02: Descriptive Statistics · 2.1: Prelude to Descriptive Statistics
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Applied Statistics for Social Science
LibreTexts
In the contents: 1.2: Definitions of Statistics, Probability, and Key Terms · 02: Descriptive Statistics · 2.1: Prelude to Descriptive Statistics
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Introduction to Applied Statistics for Psychology Students
Gordon Sarty · University of Saskatchewan · 2022
Introduction to Applied Statistics for Psychology Students, by Gordon E. Sarty (Professor, Department of Psychology, University of Saskatchewan) began as a textbook published in PDF format, in various editions between 2014-2017. The book was written to meet the needs of University of Saskatchewan psychology students at the undergraduate (PSY 233, PSY 234) level. In 2019-2020, funding was provided through the Gwenna Moss Centre for Teaching and Learning, along with technical assistance from the Distance Education Unit, to update and adapt this book, making it more widely available in an easy-to-use and more adaptable digital (Pressbooks) format. The update also made revisions so that the book could be published with a license appropriate for open educational resources (OER). OERs are defined as “teaching, learning, and research resources that reside in the public domain or have been released under an intellectual property license that permits their free use and re-purposing by others” (Hewlett Foundation). This textbook and other OERs like it are openly licensed using a Creative Commons license, and are offered in various digital and e-book formats free of charge. Printed editions of this book can be obtained for a nominal fee through the University of Saskatchewan bookstore.
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Applied Probability
Paul Pfeiffer · Rice University
In the contents: 13.03: Simple Random Samples and Statistics · 17.01: Appendix A to Applied Probability- Directory of m-functions and m-procedures · 17.02: Appendix B to Applied Probability- some mathematical aids
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Natural Resources Biometrics
Diane Kiernan · SUNY College of Environmental Science and Forestry
In the contents: 01: Descriptive Statistics and the Normal Distribution · 1.01: Descriptive Statistics
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Introduction to Statistics in the Psychological Sciences
Linda R. Cote, Rupa G. Gordon, Chrislyn E. Randell, Judy Schmitt, and Helena Marvin · LibreTexts
In the contents: 01: Unit 1- Fundamentals of Statistics
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Quantitative Research Methods for Political Science, Public Policy and Public Administration
Jenkins-Smith et al. · University of Oklahoma
The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. It is designed for advanced undergraduate courses, or introductory and intermediate graduate-level courses.
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Basic Statistics Using R for Crime Analysis
Jaeyong Choi · West Chester University
In the contents: 03: Creating a New Variable and Producing Summary Statistics
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Biological Statistics
John H. McDonald · University of Delaware
In the contents: 03: Descriptive Statistics · 3.01: Statistics of Central Tendency · 3.02: Statistics of Dispersion
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An Introduction to Psychological Statistics
Foster et al. · LibreTexts
In the contents: 1.01: What are statistics · 1.02: Why do we study statistics
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Learning Statistics with R - A tutorial for Psychology Students and other Beginners
DJ Navarro · University of New South Wales
In the contents: 01: Why Do We Learn Statistics · 1.01: On the Psychology of Statistics · 1.03: Statistics in Psychology
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Statistics for Research Students
Erich C Fein, John Gilmour, Tayna Machin, and Liam Hendry · University of Southern Queensland
In the contents: 1.02: Descriptive Statistics · 02: Test Statistics p Values Confidence Intervals and Effect Sizes · 09: Nonparametric Statistics
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Mike’s Biostatistics Book
Michael R Dohm · Chaminade University
In the contents: 2.1: Why (Bio)Statistics · 2.3: A brief history of (bio)statistics · 2.4: Experimental Design and rise of statistics in medical research
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A Contemporary Approach to Research and Statistics in Psychology
Klaire Somoray · James Cook University
In the contents: 1.01: What can Statistics do for us · 1.03: Big Ideas in Statistics · 09: Beyond Research and Statistics
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Statistical Thinking for the 21st Century
Russell Poldrack · Russell Poldrack · 2018
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.
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Integral Calculus with Applications to Life Sciences
Paul Tsopméné · University of British Columbia Library · 2025
Integral Calculus with Applications to Life Sciences is an open educational resource (OER) that covers an unusually broad range of topics—including integration, infinite series, differential equations, linear algebra, probability, and statistics—rarely found in a single textbook. Each section begins with clear learning outcomes and a motivational paragraph, often tied to applications in the life sciences, helping students see the immediate relevance of what they learn. Examples are worked out in full detail. Every time a calculus or algebraic rule is applied, it is explicitly stated at the point of use. Rather than isolating algebra review in a preliminary chapter, this text integrates algebra throughout, allowing students to reinforce foundational skills while learning new material. This approach is especially supportive for students who may struggle with algebra. Practice problems with complete solutions follow each example, and most sections end with an application to biology, medicine, or related fields. The exercise sections include not only standard questions but also critical thinking problems such as finding errors in worked solutions, constructing counterexamples, and solving real-world problems. The text includes final answers for all exercises at the back. Hyperlinks allow students to jump seamlessly between exercises and final answers. By combining clarity, rigor, accessibility, and real-world relevance, one of the main goals of this OER is to help students not only to succeed in calculus but also to appreciate its power in understanding the living world.
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Mostly Harmless Elementary Statistics
Rachel Webb · Portland State University Library · 2023
This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples provided for Microsoft Excel, TI-84 & TI-89 calculators. Students new to statistics are sure to benefit from this ADA accessible and relevant textbook. The examples are current and resonate with everyday life. The casual narrative style, has a conversational tone to provide an inclusive and easy to read format for students.
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Intermediate Statistics with R
Mark Greenwood · Montana State University · 2021
Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.
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PSYC 2200: Elementary Statistics for the Behavioral and Social Sciences
Michelle Oja · LibreTexts · 2021
Welcome to behavioral statistics, a statistics textbook for social science majors!