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
• 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