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TOUR OF
STATISTICAL
PACKAGES
RESEARCH HUB AT THE UNIVERSITY LIBRARIES
PENN STATE UNIVERSITY
OVERVIEW
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Explore six different common statistical software packages
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Overview
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Common fields
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Pros and cons
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General usage
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Examples
Where can we use these on campus?
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Additional resources
PACKAGES
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R
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SAS
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Minitab
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JMP
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STATA
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SPSS
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Others not explored: Excel, MATLAB, Stat-Ease, SQL, Nvivo, AMOS, S-plus
WHERE CAN WE USE
THESE ON CAMPUS?
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R is free and can be downloaded in both permanent and portable forms online
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All those explored here can be found at all labs on campus
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Find labs at http://clc.its.psu.edu/labs/locations
Nvivo (not explored) is only found in Hammond 317 and Sparks 6
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The following can be found on WebApps:
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Excel
Minitab
SAS
JMP
MATLAB
ADDITIONAL RESOURCES
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Research Hub:
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• Training and tutorials
• Consulting for data, statistics, and GIS
• Research guides
• Data management toolkit
• Other services
• http://www.libraries.psu.edu/psul/researchhub.html
Quick tutorials in Minitab, SAS, R, and SPSS:
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• http://stat.psu.edu/education/quicktutorials
Statistical Consulting Center:
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• http://stat.psu.edu/consulting/statistical-consulting-center
Survey Research Center:
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http://www.ssri.psu.edu/survey
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Penn State Census Research Data Center (coming soon)
EXPLORING R
R: OVERVIEW
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Free, open-source software; similar to S-plus
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Multiple add-ons and extensions available, including integration with LaTeX ( a word
processor) via RStudio, and Excel via RExcel
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Extensive online help manuals and forums
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Used by many statisticians and computer scientists for data mining, data analysis, and
development of statistical methodology
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Case-sensitive language
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Common fields:
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Statistical science
Computational biology
Computer science
Quantitative finance
Engineering
R: PROS AND CONS
Pros:
Cons:
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Widely used in both industry and academia
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Scripting programming language
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Flexible and customizable analyses and
graphics
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Mediocre graphics
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Not as useful for:
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Great for:
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Data manipulation, editing, and coding
Data mining
Simulations
Survival analysis
Linear and nonlinear modeling
Data warehousing
Multivariate analysis
Nonparametric methods
Hypothesis testing
Categorical analysis
Time series analysis
Sample size calculation/power analysis
Optimization
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Graphical analysis
Data summary
Exploratory analysis
Quality assessment and improvement
Design of experiments
R: USAGE
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Data can be read in through code or created
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Variables and functions can be created and renamed
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Multiple data sets can be handled at once
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Editor window is used to write and save commands
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Console window reads commands and displays output, which is best saved by
copying and pasting into a word processing document
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Graphs are outputted in separate window, which is overwritten for each new graph
unless otherwise indicated in commands
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Workspaces can be saved, meaning data sets and variables do not need to be
recreated (especially useful if data creation and manipulation take a long time to
run)
R: EXAMPLES
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Read in data set from a text file
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Create a variable
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Find online help
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Run a t-test
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Create a histogram
R: EXAMPLES
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Read in data set from a text file
R: EXAMPLES
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Create a variable
R: EXAMPLES
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Find online help
R: EXAMPLES
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Run a t-test
R: EXAMPLES
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Create a histogram
EXPLORING SAS
SAS: OVERVIEW
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Major statistical software in many industries
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Multiple add-ons and extensions available, including integration of SQL programming language
and integration with JMP
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Extensive online help manuals and forums
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Used by many statisticians and computer scientists for data mining, data analysis, and
development of statistical methodology
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Not case-sensitive language
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Offers various certifications, which many employers value highly
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Common fields:
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Statistical science
Sociology
Manufacturing
Pharmaceutical science
Agriculture
Computer science
Quantitative finance
Engineering
SAS: PROS AND CONS
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Pros:
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Widely used in both industry and academia
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High-performance architecture that
supports computationally-intensive
algorithms
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Flexible and customizable analyses and
graphics
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Great for:
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Data manipulation, editing, and coding
Data mining
Graphical analysis
Data summary
Exploratory analysis
Simulations
Forecasting
Survival analysis
Linear and nonlinear modeling
Quality assessment and improvement
Data warehousing
Multivariate analysis
Nonparametric methods
Hypothesis testing
Categorical analysis
Time series analysis
Sample size calculation/power analysis
Design of experiments
Optimization
Cons:
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Scripting programming language
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Expensive
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Some versions are not 100% compatible
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Not as useful for:
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Simple analysis and manipulation
SAS: USAGE
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Data can be read in through a command or imported through menu-driven prompts
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Variables and functions can be created and renamed
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Multiple data sets can be handled at once and are stored in various workspaces
(“libraries”)
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Four types of commands: DATA step (read & edit data); Procedure steps (run built-in
functions); macros (create and run own function); ODS statements (set output
settings, styles, etc.)
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Editor window is used to write and save commands
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Log window reads commands and displays any errors or comments
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Output window displays some output created by commands
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Results viewer window displays most output, including graphs
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Can save only commands, only data, or whole project
SAS: EXAMPLES
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Import data from a text file
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Display data set
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Create new data set and add a variable
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Run a regression with diagnostic plots
SAS: EXAMPLES
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Import data from a text file
SAS: EXAMPLES
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Import data from a text file
SAS: EXAMPLES
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Display data set
SAS: EXAMPLES
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Create new data set and add a variable
SAS: EXAMPLES
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Run a regression with diagnostic plots
SAS: EXAMPLES
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Run a regression with diagnostic plots
EXPLORING MINITAB
MINITAB: OVERVIEW
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Menu-driven statistical software, but does have scripting language available for typing
commands or creating macros
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Used in most Six Sigma courses and workshops
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Help documentation located in software as well as online
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Used by many analysts to quantitatively make decisions
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Common fields:
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Social science
Marketing
Education
Sociology
Manufacturing
Agriculture
Pharmaceutical science
Engineering
MINITAB: PROS AND CONS
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Pros:
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Commonly used in industry and some
academic settings
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Easy-to-use menu-driven software
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Clear output and graphics with some
interactive features
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Has an “Assistant” feature that includes flowcharts and takes users step-by-step to
analyze data properly
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Used in most undergraduate statistics
courses; there are example data sets
included in software
Cons:
Limited options for analyses
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Can only analyze one data set at a time
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Does not work as well with large data sets
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Not as much help available as some other
packages
Great for:
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Data manipulation, editing, and coding
Graphical analysis
Exploratory data analysis
Data summary
Forecasting
Survival analysis
Linear and nonlinear modeling (standard)
Quality assessment and improvement
Hypothesis testing
Categorical analysis
Time series analysis
Design of experiments
Optimization
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Not as useful for:
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Simulations
Data mining
Data warehousing
Multivariate analysis
Nonparametric methods
Sample size calculation/power analysis
Advanced or complex modeling
MINITAB: USAGE
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Data can be typed in, copied and pasted from a text or Excel file, or imported
through menu-driven prompts
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New variables can be added to worksheet or created using formulas
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Worksheets contain raw data and only one worksheet can be active at a time
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Can create and save macros and/or commands
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Session window displays output
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Graphs and other visual charts are shown in individual windows
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Project manager contains outline that helps you to jump to particular output
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Worksheet can be saved separately, but saving whole project will save both
worksheet and output
MINITAB: EXAMPLES
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Copy data into Minitab from a text file
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Create a new variable using formula
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Use Assistant to do a graphical analysis
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Create a factorial design for an experiment
MINITAB: EXAMPLES
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Copy data into Minitab from a text file
MINITAB: EXAMPLES
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Create a new variable using formula
MINITAB: EXAMPLES
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Use Assistant to do a graphical analysis
MINITAB: EXAMPLES
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Use Assistant to do a graphical analysis
MINITAB: EXAMPLES
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Use Assistant to do a graphical analysis
MINITAB: EXAMPLES
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Create a factorial design for an experiment
MINITAB: EXAMPLES
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Create a factorial design for an experiment
EXPLORING JMP
JMP: OVERVIEW
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Menu-driven statistical software, but does have scripting language available for typing
commands or creating macros
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Can integrate with SAS, including running SAS commands, importing or exporting SAS data
sets, and opening SAS projects
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Help documentation located in software as well as online
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Common fields:
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Statistical science
Manufacturing
Pharmaceutical science
Engineering
JMP: PROS AND CONS
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Pros:
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Easy-to-use menu-driven software
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Many menu option windows are interactive
and intuitive
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Powerful software with more options than
other menu-driven software
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Output and graphs are very customizable and
interactive, with options even after running
the analysis
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Great for:
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Data manipulation, editing, and coding
Graphical analysis
Exploratory data analysis
Data summary
Forecasting
Survival analysis
Linear and nonlinear modeling (standard)
Quality assessment and improvement
Multivariate analysis
Categorical analysis
Nonparametric methods
Time series analysis
Sample size calculation/power analysis
Design of experiments
Optimization
Cons:
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Not as widely used as some other packages
but still very powerful
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Can only analyze one data set at a time
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Does not work as well with large data sets
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Not as much help available as some other
packages
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Not as useful for:
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Simulations
Data mining
Data warehousing
Hypothesis testing
Advanced or complex modeling
JMP: USAGE
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Data can be typed in, copied and pasted from a text or Excel file, imported from SAS,
or converted from other files (such as a .txt, etc.)
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New variables can be added to worksheet or created using formulas
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Data tables contain raw data and only one data table can be active at a time
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Can create and save macros and/or commands
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Log window allows you to input commands and view output
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Script window contains the commands used to run the same analysis done through
the menu-driven prompts
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Each data table will create its own output window for graphs and other output
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Data tables and projects are saved separately
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Graphics and other output can be saved into a Journal, which is saved separately
and can be opened in Word, etc., making it convenient to store results
JMP: EXAMPLES
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Convert text file into a JMP data table
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Summarize group means
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Change table values from mean values to standard deviation values
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Fit a binary logistic regression model
JMP: EXAMPLES
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Convert text file into a JMP data table
JMP: EXAMPLES
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Summarize group means
JMP: EXAMPLES
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Summarize group means
JMP: EXAMPLES
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Change table values from mean values to standard deviation values
JMP: EXAMPLES
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Fit a binary logistic regression model
EXPLORING STATA
STATA: OVERVIEW
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Utilizes both menu-driven selections and scripting commands
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Multiple versions available depending on needs (commercial, educational, etc.)
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Extensive help documentation and technical support
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Contains both basic and advanced statistical methods
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Not case-sensitive language
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Common fields:
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Economics
Sociology
Political science
Pharmaceutical
Epidemiology
STATA: PROS AND CONS
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Pros:
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Somewhat common in both industry and
academia
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Somewhat flexible and customizable
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Contains up-to-date advanced methods
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Quality graphics
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Great for:
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Data manipulation, editing, and coding
Graphical analysis
Data summary
Exploratory analysis
Data mining
Simulations
Survival analysis
Linear and nonlinear modeling
Data warehousing
Multivariate analysis
Nonparametric methods
Hypothesis testing
Categorical analysis
Time series analysis
Sample size calculation/power analysis
Cons:
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Scripting programming language
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Can only analyze one data set at a time
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Does not work as well with large data sets
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Not as useful for:
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Quality assessment and improvement
Design of experiments
Optimization
STATA: USAGE
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Data can be typed in, read in through code, copied and pasted from a text or Excel
file, or imported and converted from other files (such as a .txt, etc.)
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Command window is used to write and run commands
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Review window displays previous analysis, which can be selected to run again
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Project window displays all input and output, including graphs
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Store and edit data in the Data Editor, which can be saved on its own
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Log will copy and automatically save the project for you (must start and close log
before and after the analyses you want to save)
STATA: EXAMPLES
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Copy data from a text file into STATA
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Recode variable
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Create a frequency table using commands
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Run a Wilcoxon Rank-Sum test using menu options
STATA: EXAMPLES
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Copy data from a text file into STATA
STATA: EXAMPLES
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Recode variable
STATA: EXAMPLES
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Create a frequency table using commands
STATA: EXAMPLES
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Run a Wilcoxon Rank-Sum test using menu options
STATA: EXAMPLES
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Run a Wilcoxon Rank-Sum test using menu options
EXPLORING SPSS
SPSS: OVERVIEW
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Menu-driven statistical software, but does have scripting language available for typing
commands or creating macros
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Used in conjunction with many common survey platforms, and is the leading software for
analyzing survey data
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Help documentation located in software as well as online
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Plug-ins available for other programming languages, such as JAVA, Python, R, and VB
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Used by many analysts to quantitatively make decisions
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Common fields:
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Social science
Marketing
Education
Sociology
Healthcare
Government
SPSS: PROS AND CONS
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Pros:
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Commonly used in industry, especially those
that utilize survey data
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Easy-to-use menu-driven software
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Output and graphics are clear and wellorganized
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Separate “Data” and “Variable” tabs in data
worksheet make it easy to switch from raw
data to variable information (labels, codes,
variable type, etc.)
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Can use other programing languages
(Python, R, JAVA, VB) with plug-ins
Great for:
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Data manipulation, editing, and coding
Graphical analysis
Exploratory data analysis
Data summary
Data warehousing
Forecasting
Linear and nonlinear modeling (standard)
Quality assessment and improvement
Hypothesis testing
Multivariate analysis
Nonparametric methods
Categorical analysis
Time series analysis
Cons:
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Limited options for analyses
•
Can only analyze one data set at a time
•
Not as much help available as some other
packages
•
Not as useful for:
•
•
•
•
•
•
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Simulations
Data mining
Survival analysis
Sample size calculation/power analysis
Advanced or complex modeling
Design of experiments
Optimization
SPSS: USAGE
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Data can be typed in, copied and pasted from a text or Excel file, imported through
menu-driven prompts, or read in from a ASCII file using Syntax editor
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New variables can be added to worksheet or created using formulas
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Datasets contain raw data and only one dataset can be active at a time
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Can create and save macros and/or commands
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Output window displays output, including graphs
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Output can be copied and pasted into other documents
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Project manager contains outline that helps you to jump to particular output
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Dataset and Outputs are saved separately
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Optional syntax window can read and run commands and can also be saved
separately
SPSS: EXAMPLES
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Cody data from text file into SPSS spreadsheet
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Edit variable names and information
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Create a contingency table
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Fit a linear model
SPSS: EXAMPLES
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Cody data from text file into SPSS spreadsheet
SPSS: EXAMPLES
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Edit variable names and information
SPSS: EXAMPLES
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Edit variable names and information
SPSS: EXAMPLES
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Create a contingency table
SPSS: EXAMPLES
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Create a contingency table
SPSS: EXAMPLES
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Fit a linear model
SPSS: EXAMPLES
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Fit a linear model