Statistics & Research Methods

Browse statistics, biostatistics, probability, and research methodology textbooks for graduate students, researchers, and data analysts. Includes intro statistics, advanced methods, multivariate analysis, and applied statistics with R, SPSS, and SAS.

What's included

Introductory and advanced statistics textbooks
Biostatistics for medical and public health research
Probability theory and stochastic processes
Research design and methodology guides
Multivariate analysis, regression, and Bayesian methods
Testing & measurement and psychometrics

Who it's for

PhD and master's students in social sciences, public health, psychology, and biostatistics; data analysts; clinical researchers; and faculty teaching quantitative methods.

Why Book Shop Now

Authentic editions from Springer, Routledge, CRC Press, and Sage. Verified ISBNs and fast US shipping.

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Reliability Engineering and Risk Analysis A Practical Guide by Mohammad Modarres

Discovering Statistics Using IBM SPSS Statistics by Andy Field

Generalized Additive Models An Introduction with R by Simon N. Wood

Using the R Commander A Point-and-click Interface for R by John Fox

Statistics with R A Beginner's Guide by Robert Stinerock

Statistics for Engineering and the Sciences, Sixth Edition Student Solutions Manual by William M. Mendenhall

Applied Reliability, Third Edition by Paul A. Tobias

A Gentle Introduction to Stata by Alan C. Acock

Modelling and Quantitative Methods in Fisheries, Second Edition by Malcolm Haddon

Adventures in Social Research Data Analysis Using IBM SPSS Statistics by Earl R. Babbie

Bayesian Methods for Data Analysis, Third Edition by Bradley P. Carlin

Deep Learning Architectures A Mathematical Approach by Ovidiu Calin

An Introduction to Statistics with Python With Applications in the Life Sciences by Thomas Haslwanter

Linear and Nonlinear Programming by David G. Luenberger

R For Marketing Research and Analytics by Chris Chapman

The Algorithm Design Manual by Steven S. Skiena

Computer Vision Algorithms and Applications by Richard Szeliski

Econometric Analysis of Panel Data by Badi H. Baltagi

Machine Learning in Finance From Theory to Practice by Matthew F. Dixon

Design of Observational Studies by Paul R. Rosenbaum

Machine Learning Crash Course for Engineers by Eklas Hossain

Business Statistics for Competitive Advantage with Excel 2019 and JMP Basics, Model Building, Simulation and Cases by Cynthia Fraser

Neural Networks and Deep Learning A Textbook by Charu C. Aggarwal

Linear Algebra and Optimization for Machine Learning A Textbook by Charu C. Aggarwal