You are here

Statistical Analysis Resources

Last Updated: 11-May-2015

Research Skills

Find statistics

For analysis of statistics data, you typically use software such as R, SPSS, Stata, SAS, JMP or even Excel. This guide briefly discusses these software packages and lists several places on campus to get assistance with their use.  Also included are links to relevant books and to a table that may help you decide which type of statistical analysis is best for your project.

Subject Librarians

Kathleen Gust
Kathleen Gust
Engineering Librarian for Outreach Instruction and Electronic Resources
Engineering Library (Terman)
(650) 723-8877
Michael Newman
Michael L Newman
Head Librarian and Bibliographer
Biology Library (Falconer)
(650) 723-1110

Subjects

Engineering
Mathematics and statistics

Related guides

Mathematics and statistics

Around Stanford

Social Science Data and Software
Data Management Services

General Resources

Cambridge dictionary of statistics
Simple, reliable definitions and explanations of modern statistical and statistics-related concepts.
Current index to statistics (CIS)
A primary index for the statistical sciences; there is some overlap with MathSciNet. Covers articles in journals, edited books and proceedings from before 1975 up to roughly last year.
Encyclopedia of statistical sciences
A reference guide for those working in the area of quality control, economics, probability, and statistics as well as those applying statistical methods in almost any discipline.
International encyclopedia of statistical science
Articles contributed by distinguished statisticians and educators from around the world. Offers quick, comprehensive and highly accessible information on statistical terms, methods and applications.
Data analysis examples
A general table from UCLA, matching up kinds of variables with possible analysis styles.
What statistical analysis shall I use?
Another table from UCLA with examples (often hypothetical) illustrating the application of different statistical analysis techniques using different statistical packages.

Statistical Packages

Here is a brief comparison of some statistical software packages.

JMP – Used in applications such as Six Sigma, quality control and engineering, design of experiments and scientific research.  Easy to use, links statistics with graphics to interactively explore, understand, and visualize data. Installed at Lane Medical Library, otherwise purchase.

R- Free open source programming language for statistical computing with many variants.  Many add-ons available, steep learning curve, no GUI. Provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc. Installed on Library and Residential Cluster machines and Farmshare, otherwise see Installing R.   Free downloads available at:  R Software and R Studio.

SAS – Powerful software suite, suitable for complex data sets.  Steep learning curve, but good documentation and help files. Installed on Library and Residential Cluster machines and Farmshare, otherwise purchase.

SPSS – Software package used for statistical analysis.  Menu-driven like Excel.  Not for analyzing complex survey data. Installed on Library and Residential Cluster machines, otherwise purchase.

Stata - Software package uses either menus or command-line interface. Capabilities include data management, statistical analysis, graphics, simulations, regression analysis (linear and multiple), and custom programming.  Robust support for complex sample surveys. Installed on Library and Residential Cluster machines and Farmshare, otherwise purchase.

Campus Data and Statistics Assistance

Campus resources offered by Stanford University Libraries or led by faculty and interested graduate students.

SSDS (Social Science Data and Software)
Provides help with selecting and using quantitative software (SAS, Stata, SPSS, R).  In addition, they provide support using software during drop-in hours, via email and by appointment.

CSquared - Computational Consulting
Advice on a range of topics in computational mathematics, including (but not limited to):
•    Data Science
•    Matrix Problems
•    Optimization
•    Discrete Mathematics
•    PDEs & Physical Simulation
•    High Performance Computing
•    Machine Learning

Data Science Drop-In
Help with all aspects of data collection, cleaning, analysis, and visualization. Fill in the short check-in form on their site to prepare for a visit.
•    APIs & web crawling
•    Parsing unstructured data
•    Online experiments
•    Data storage and querying
•    Distributed computing & scalable algorithms
•    Large-scale regression & classification
•    Natural language processing
•    Effective visual communication

Department of Statistics Consulting
Drop by, especially in the early stages of your research. At any stage, of course, you may bring data, prior analyses, or any other relevant material. Please complete the client information form on their website to help them prepare for your visit.
•    Experimental design
•    Data analysis and interpretation of results
•    Model fitting
•    Time series
•    Classification and prediction

Books

Many of the titles listed below are available in electronic formats. 
** Note: The SpringerLink online version of this book, also allows user to order a print on demand paperback purchase through the My Copy program.

** Applied statistics using SPSS, STATISTICA, MATLAB and R
This reference provides a comprehensive introduction and tutorial on the main statistical analysis topics, demonstrating their solution with the most common software package. The book explains and shows how to use SPSS, MATLAB, STATISTICA and R for analysis such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics.
** Exploratory data analysis in business and economics
The book shows how to fit and interpret regression models for categorical data (ie., binary, ordinal and count outcome) with Stata, and provides a suite of commands for hypothesis testing and model diagnostics. All the examples, datasets, and author-written commands are available from the authors' web site.
Practical data analysis with JMP
Uses the powerful interactive and visual approach of JMP to introduce readers to the logic and methods of statistical thinking and data analysis.
JMP for basic univariate and multivariate statistics
Offers clearly written instructions to guide you through the basic concepts of research and data analysis, enabling you to easily perform statistical analyses and solve problems in real-world research.
JMP essentials: an illustrated step-by-step guide for new users
Designed for the new or occasional JMP user who needs to generate meaningful graphs or results quickly. 
JMP start statistics
Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises.
The R book
The book provides comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities. The author also Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test and proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more.
Learning statistics using R
Covering a wide range of topics, from probability and sampling distribution to statistical theorems and chi-square, this introductory book helps readers learn not only how to use formulae to calculate statistics, but also how specific statistics fit into the overall research process.
A survivor's guide to R
Easy-to-read approach helps students with little or no background in statistics or programming to develop real-world R skills through straightforward coverage of R objects and functions.
A handbook of statistical analyses using R
The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.
R data analysis without programming
Prepares readers to analyze data and interpret statistical results using R. By introducing R with LessR extensions, readers learn how to organize data for analysis, read the data into R, and produce output without performing numerous functions and programming exercises first.
The little SAS book: a primer
This friendly, easy-to-read guide introduces you to the most commonly used features of SAS software plus a whole lot more!
SAS and R: data management, statistical analysis, and graphics
How to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications.
SPSS survival manual
An excellent introduction to using SPSS for data analysis. It provides a self-contained resource itself, with more than simply (detailed and clear) step-by-step descriptions of statistical procedures in SPSS. There is also a wealth of tips and advice, and for each statistical technique a brief, but consistently reliable, explanation is provided. In this third edition all chapters have been updated to accommodate changes to SPSS procedures, screens and output in version 15. Additional data files are available on the book's supporting website.
Discovering statistics using IBM SPSS statistics
This text is notable for the breadth of statistics covered and for seamless meshing this content with SPSS. Topics include linear regression, logistic regression, ANOVA, ANCOVA, hypothsis testing, non-parametric tests, factor analysis, categorical data etc. For additional materials (ppt, SPSS movies, etc) visit the companion website.
** Data analysis in management with SPSS software
Focuses on providing readers with the knowledge and skills needed to carry out research in management, humanities, social and behavioral science.
Data analysis using Stata
The first chapters are devoted to becoming familiar with Stata and the essentials of effective data management. The text is also a valuable companion reference for more advanced users. It covers a host of useful applications for health researchers including the analysis of stratified data via epitab and regression models; linear, logistic, and Poisson regression; survival analysis including Cox regression, standardized rates, and correlation/ROC analysis of measurements.
Introduction to modern econometrics using Stata
This book covers topics often omitted from basic introductions to Stata, including linear regression, GLS, instrumental variables, linear panel-data, logit model, tobit model. The authors address each topic with an in-depth Stata example and demonstrate how to use Stata’s programming features to implement methods for which Stata does not have a specific command.
Interpreting and visualizing regression models using Stata
A clear treatment of how to carefully present results from model-fitting in a wide variety of settings. It is a boon to anyone who has to present the tangible meaning of a complex model in a clear fashion, regardless of the audience.