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Statistical Analysis Resources

Last Updated: 30-Sep-2015

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
Engineering Librarian for Outreach Instruction and Electronic Resources
(650) 723-8877
Michael Newman
Head Librarian and Bibliographer
(650) 723-1110

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

Stanford Data Lab
Helping students develop their data skills by solving real problems with expert coaching. The problems range from simple challenges to master data fundamentals, up to working on unsolved high-impact problems for NGOs, government agencies, and other social sector organizations.