RESEARCH HUB AT THE UNIVERSITY LIBRARIES

PENN STATE UNIVERSITY

TOUR OF STATISTICAL PACKAGES

OVERVIEW• Explore six different common statistical software packages

• Overview• Common fields• Pros and cons• General usage• Examples

• Where can we use these on campus?

• Additional resources

PACKAGES• R

• SAS

• Minitab

• JMP

• STATA

• SPSS

• Others not explored: Excel, MATLAB, Stat-Ease, SQL, Nvivo, AMOS, S-plus

WHERE CAN WE USE THESE ON CAMPUS?

• R is free and can be downloaded in both permanent and portable forms online

• All those explored here can be found at all labs on campus

• Find labs at http://clc.its.psu.edu/labs/locations• Nvivo (not explored) is only found in Hammond 317 and Sparks 6

• The following can be found on WebApps:

• Excel• Minitab• SAS• JMP• MATLAB

ADDITIONAL RESOURCES• Research Hub:

• 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:

• http://stat.psu.edu/education/quicktutorials• Statistical Consulting Center:

• http://stat.psu.edu/consulting/statistical-consulting-center• Survey Research Center:

• http://www.ssri.psu.edu/survey

• HHD Methodology Consulting Group:

• http://www.hhdev.psu.edu/dsg/Methodology-Consulting-Group

• Penn State Census Research Data Center (coming soon)

EXPLORING R

R: OVERVIEW• Free, open-source software; similar to S-plus

• Multiple add-ons and extensions available, including integration with LaTeX ( a word processor) via RStudio, and Excel via RExcel

• Extensive online help manuals and forums

• Used by many statisticians and computer scientists for data mining, data analysis, and development of statistical methodology

• Case-sensitive language

• Common fields:

• Statistical science• Computational biology• Computer science• Quantitative finance• Engineering

R: PROS AND CONSPros:

• Widely used in both industry and academia

• Flexible and customizable analyses and graphics

• Great for:

• 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

Cons:

• Scripting programming language

• Mediocre graphics

• Not as useful for:

• Graphical analysis• Data summary• Exploratory analysis• Quality assessment and improvement• Design of experiments

R: USAGE• Data can be read in through code or created

• Variables and functions can be created and renamed

• Multiple data sets can be handled at once

• Editor window is used to write and save commands

• Console window reads commands and displays output, which is best saved by copying and pasting into a word processing document

• Graphs are outputted in separate window, which is overwritten for each new graph unless otherwise indicated in commands

• 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• Read in data set from a text file

• Create a variable

• Find online help

• Run a t-test

• Create a histogram

R: EXAMPLES• Read in data set from a text file

R: EXAMPLES• Create a variable

R: EXAMPLES• Find online help

R: EXAMPLES• Run a t-test

R: EXAMPLES• Create a histogram

EXPLORING SAS

SAS: OVERVIEW• Major statistical software in many industries

• Multiple add-ons and extensions available, including integration of SQL programming language and integration with JMP

• Extensive online help manuals and forums

• Used by many statisticians and computer scientists for data mining, data analysis, and development of statistical methodology

• Not case-sensitive language

• Offers various certifications, which many employers value highly

• Common fields:

• Statistical science• Sociology• Manufacturing• Pharmaceutical science• Agriculture• Computer science• Quantitative finance• Engineering

SAS: PROS AND CONSPros:

• Widely used in both industry and academia

• High-performance architecture that supports computationally-intensive algorithms

• Flexible and customizable analyses and graphics

• Great for:

• 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:

• Scripting programming language

• Expensive

• Some versions are not 100% compatible

• Not as useful for:

• Simple analysis and manipulation

SAS: USAGE• Data can be read in through a command or imported through menu-driven prompts

• Variables and functions can be created and renamed

• Multiple data sets can be handled at once and are stored in various workspaces (“libraries”)

• 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.)

• Editor window is used to write and save commands

• Log window reads commands and displays any errors or comments

• Output window displays some output created by commands

• Results viewer window displays most output, including graphs

• Can save only commands, only data, or whole project

SAS: EXAMPLES• Import data from a text file

• Display data set

• Create new data set and add a variable

• Run a regression with diagnostic plots

SAS: EXAMPLES• Import data from a text file

SAS: EXAMPLES• Import data from a text file

SAS: EXAMPLES• Display data set

SAS: EXAMPLES• Create new data set and add a variable

SAS: EXAMPLES• Run a regression with diagnostic plots

SAS: EXAMPLES• Run a regression with diagnostic plots

EXPLORING MINITAB

MINITAB: OVERVIEW• Menu-driven statistical software, but does have scripting language available for typing

commands or creating macros

• Used in most Six Sigma courses and workshops

• Help documentation located in software as well as online

• Used by many analysts to quantitatively make decisions

• Common fields:

• Social science • Marketing• Education• Sociology• Manufacturing• Agriculture• Pharmaceutical science• Engineering

MINITAB: PROS AND CONSPros:

• Commonly used in industry and some academic settings

• Easy-to-use menu-driven software

• Clear output and graphics with some interactive features

• Has an “Assistant” feature that includes flow-charts and takes users step-by-step to analyze data properly

• Used in most undergraduate statistics courses; there are example data sets included in software

• Great for:

• 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

Cons:

Limited options for analyses

• Can only analyze one data set at a time

• Does not work as well with large data sets

• Not as much help available as some other packages

• Not as useful for:

• Simulations• Data mining• Data warehousing• Multivariate analysis• Nonparametric methods• Sample size calculation/power analysis• Advanced or complex modeling

MINITAB: USAGE• Data can be typed in, copied and pasted from a text or Excel file, or imported

through menu-driven prompts

• New variables can be added to worksheet or created using formulas

• Worksheets contain raw data and only one worksheet can be active at a time

• Can create and save macros and/or commands

• Session window displays output

• Graphs and other visual charts are shown in individual windows

• Project manager contains outline that helps you to jump to particular output

• Worksheet can be saved separately, but saving whole project will save both worksheet and output

MINITAB: EXAMPLES• Copy data into Minitab from a text file

• Create a new variable using formula

• Use Assistant to do a graphical analysis

• Create a factorial design for an experiment

MINITAB: EXAMPLES• Copy data into Minitab from a text file

MINITAB: EXAMPLES• Create a new variable using formula

MINITAB: EXAMPLES• Use Assistant to do a graphical analysis

MINITAB: EXAMPLES• Use Assistant to do a graphical analysis

MINITAB: EXAMPLES• Use Assistant to do a graphical analysis

MINITAB: EXAMPLES• Create a factorial design for an experiment

MINITAB: EXAMPLES• Create a factorial design for an experiment

EXPLORING JMP

JMP: OVERVIEW• Menu-driven statistical software, but does have scripting language available for typing

commands or creating macros

• Can integrate with SAS, including running SAS commands, importing or exporting SAS data sets, and opening SAS projects

• Help documentation located in software as well as online

• Common fields:

• Statistical science• Manufacturing• Pharmaceutical science• Engineering

JMP: PROS AND CONSPros:

• Easy-to-use menu-driven software

• Many menu option windows are interactive and intuitive

• Powerful software with more options than other menu-driven software

• Output and graphs are very customizable and interactive, with options even after running the analysis

• Great for:

• 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:

• Not as widely used as some other packages but still very powerful

• Can only analyze one data set at a time

• Does not work as well with large data sets

• Not as much help available as some other packages

• Not as useful for:

• Simulations• Data mining• Data warehousing• Hypothesis testing• Advanced or complex modeling

JMP: USAGE• 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.)

• New variables can be added to worksheet or created using formulas

• Data tables contain raw data and only one data table can be active at a time

• Can create and save macros and/or commands

• Log window allows you to input commands and view output

• Script window contains the commands used to run the same analysis done through the menu-driven prompts

• Each data table will create its own output window for graphs and other output

• Data tables and projects are saved separately

• 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• Convert text file into a JMP data table

• Summarize group means

• Change table values from mean values to standard deviation values

• Fit a binary logistic regression model

JMP: EXAMPLES• Convert text file into a JMP data table

JMP: EXAMPLES• Summarize group means

JMP: EXAMPLES• Summarize group means

JMP: EXAMPLES• Change table values from mean values to standard deviation values

JMP: EXAMPLES• Fit a binary logistic regression model

EXPLORING STATA

STATA: OVERVIEW• Utilizes both menu-driven selections and scripting commands

• Multiple versions available depending on needs (commercial, educational, etc.)

• Extensive help documentation and technical support

• Contains both basic and advanced statistical methods

• Not case-sensitive language

• Common fields:

• Economics• Sociology• Political science• Pharmaceutical• Epidemiology

STATA: PROS AND CONSPros:

• Somewhat common in both industry and academia

• Somewhat flexible and customizable

• Contains up-to-date advanced methods

• Quality graphics

• Great for:

• 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:

• Scripting programming language

• Can only analyze one data set at a time

• Does not work as well with large data sets

• Not as useful for:

• Quality assessment and improvement• Design of experiments• Optimization

STATA: USAGE• 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.)

• Command window is used to write and run commands

• Review window displays previous analysis, which can be selected to run again

• Project window displays all input and output, including graphs

• Store and edit data in the Data Editor, which can be saved on its own

• 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• Copy data from a text file into STATA

• Recode variable

• Create a frequency table using commands

• Run a Wilcoxon Rank-Sum test using menu options

STATA: EXAMPLES• Copy data from a text file into STATA

STATA: EXAMPLES• Recode variable

STATA: EXAMPLES• Create a frequency table using commands

STATA: EXAMPLES• Run a Wilcoxon Rank-Sum test using menu options

STATA: EXAMPLES• Run a Wilcoxon Rank-Sum test using menu options

EXPLORING SPSS

SPSS: OVERVIEW• Menu-driven statistical software, but does have scripting language available for typing

commands or creating macros

• Used in conjunction with many common survey platforms, and is the leading software for analyzing survey data

• Help documentation located in software as well as online

• Plug-ins available for other programming languages, such as JAVA, Python, R, and VB

• Used by many analysts to quantitatively make decisions

• Common fields:

• Social science • Marketing• Education• Sociology• Healthcare• Government

SPSS: PROS AND CONSPros:

• Commonly used in industry, especially those that utilize survey data

• Easy-to-use menu-driven software

• Output and graphics are clear and well-organized

• Separate “Data” and “Variable” tabs in data worksheet make it easy to switch from raw data to variable information (labels, codes, variable type, etc.)

• Can use other programing languages (Python, R, JAVA, VB) with plug-ins

• Great for:

• 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:

• 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:

• Simulations• Data mining• Survival analysis• Sample size calculation/power analysis• Advanced or complex modeling• Design of experiments• Optimization

SPSS: USAGE• 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

• New variables can be added to worksheet or created using formulas

• Datasets contain raw data and only one dataset can be active at a time

• Can create and save macros and/or commands

• Output window displays output, including graphs

• Output can be copied and pasted into other documents

• Project manager contains outline that helps you to jump to particular output

• Dataset and Outputs are saved separately

• Optional syntax window can read and run commands and can also be saved separately

SPSS: EXAMPLES• Cody data from text file into SPSS spreadsheet

• Edit variable names and information

• Create a contingency table

• Fit a linear model

SPSS: EXAMPLES• Cody data from text file into SPSS spreadsheet

SPSS: EXAMPLES• Edit variable names and information

SPSS: EXAMPLES• Edit variable names and information

SPSS: EXAMPLES• Create a contingency table

SPSS: EXAMPLES• Create a contingency table

SPSS: EXAMPLES• Fit a linear model

SPSS: EXAMPLES• Fit a linear model