Looking for statistics books? We've gathered 23 free statistics books in PDF, covering topics like probability, descriptive statistics, biostatistics, business statistics, data science, and Bayesian inference.
These textbooks range from basic statistical analysis to advanced methods used by data scientists. Whether you need a first course or want to explore regression, probability, or machine learning, there is something here for you.
Browse our collection or explore by topic. Every book is free to read online or download as PDF.
Introductory Statistics Books
Start here if you're new to statistics. These textbooks cover the fundamentals: data types, distributions, central tendency, and basic statistical inference.
Comprehensive introduction covering descriptive statistics, probability, regression, and inferential methods. Public domain resource from Rice University with multiple contributing authors.
Comprehensive applied statistics textbook using SPSS. Covers descriptive statistics, probability, distributions, hypothesis testing, ANOVA, regression, and the General Linear Model with psychology examples.
OER textbook for behavioral and social sciences covering descriptive statistics, probability, hypothesis testing, t-tests, ANOVA, correlation, regression, and chi-square. Practical examples from psychology.
Bridges theory and implementation with topics from data display through hypothesis testing and ANOVA. Includes R-based exercises and covers regression, chi-squared tests, and nonparametric methods.
Widely adopted open-source statistics textbook covering data collection, probability, distributions, inference, and regression. Includes real-world datasets, exercises, and labs with R integration.
David Diez, Mine Cetinkaya-Rundel, Christopher Barr
Multi-university OER textbook for psychology students covering central tendency, variability, z-scores, probability, hypothesis testing, t-tests, ANOVA, correlation, regression, and chi-square.
Probability is the foundation of all statistical analysis. These books focus on distributions, random variables, and the math behind statistical reasoning.
Rigorous treatment of probability theory and statistical inference from the University of Toronto. Covers probability models, random variables, estimation, hypothesis testing, and Bayesian methods.
Advanced treatment of probability distributions and statistical methods for mathematical scientists. Covers special functions, geometrical probability, random variables, and distribution theory.
Training course slides covering R programming, descriptive statistics for univariate and multivariate data, and parametric statistical inference. Combines statistical methods with practical R implementation.
Lecture notes covering data types, sampling methods, experimental design, measures of central tendency, correlation, and confidence intervals. Focused and practical approach to descriptive methods.
Concise introduction to statistics for business contexts covering data description, probability, sampling, estimation, hypothesis testing, and regression with business-oriented examples.
Applied biostatistics covering study design, survival analysis, logistic regression, clinical trials, and epidemiological methods for healthcare and life sciences.
Where statistics meets programming and computing. These books bridge traditional statistical methods with machine learning and tools like R and Python.
Comprehensive textbook bridging statistics and machine learning for data science. Covers probability, statistics, Monte Carlo methods, regression, classification, deep learning, and unsupervised learning.
Rigorous treatment of probability and statistics tailored for data science from NYU Courant Institute. Covers probability, estimation, hypothesis testing, linear regression, and PCA with R implementation.
Accessible introduction to Bayesian statistics using computational methods in Python. Covers Bayes theorem, distributions, estimation, hypothesis testing, ABC, decision analysis, and MCMC with practical examples.
Coursera companion textbook covering Bayesian inference from foundations to advanced modeling. Includes prior elicitation, hypothesis testing, regression, and model selection using R and BAS package.
Merlise Clyde, Mine Cetinkaya-Rundel, Colin Rundel, David Banks, Christine Chai, Lizzy Huang
Concise introduction to Bayesian data analysis covering parameter estimation, model comparison, MCMC, and hierarchical models with worked examples throughout.
