Math & Statistics

OverLeaf

Overleaf is an online writing and publishing platform that supports collaborative document creation using LaTeX and rich text editing. It is commonly used for academic, scientific, and technical writing, allowing multiple contributors to work on documents simultaneously through a web browser.

Overleaf simplifies the process of creating, editing, reviewing, and publishing documents that require advanced formatting, references, equations, figures, and citations.

Key Uses

  • Write research papers, journal articles, and technical documents
  • Collaborate with co-authors on shared documents
  • Create theses, dissertations, and academic reports
  • Develop documents containing mathematical notation and scientific formatting
  • Manage references, citations, and bibliographies
  • Prepare manuscripts for publication and submission

Features

  • Online LaTeX and rich text editing
  • Real-time collaboration and document sharing
  • Automatic document compilation and preview
  • Citation and bibliography management
  • Version history and change tracking
  • Templates for academic, scientific, and professional documents
  • Access through a web browser without local software installation

Common Applications

  • Research publications
  • Grant proposals
  • Theses and dissertations
  • Technical documentation
  • Scientific manuscripts
  • Course materials and academic assignments

PTC Mathcad

PTC Mathcad Prime is an engineering calculation and documentation application that helps users perform, analyze, and share technical calculations. It combines mathematical computation, engineering notation, documentation, and visualization tools in a single workspace, enabling users to create clear, traceable engineering calculations.

Mathcad Prime enables users to document assumptions, calculations, plots, images, and supporting information in a single, easy-to-review, validate, and share format.

Key Uses

  • Perform engineering and technical calculations
  • Document and validate calculation methods
  • Analyze engineering designs and concepts
  • Create technical reports and supporting documentation
  • Share and review calculation workflows with colleagues
  • Maintain traceable engineering records

Features

  • Natural mathematical notation
  • Unit-aware calculations and dimensional analysis
  • Engineering notebook-style workspace
  • Integrated plots, charts, images, and text
  • Technical documentation and reporting tools
  • Calculation review and validation capabilities
  • Tools for sharing and collaborating on engineering work

Common Applications

  • Engineering analysis and design
  • Research and technical projects
  • Academic instruction and coursework
  • Product development
  • Technical documentation
  • Verification and validation of engineering calculations

Mplus

Mplus is a statistical modeling application used to analyze complex research data. It supports a wide range of statistical models and data types, making it useful for researchers working with cross-sectional, longitudinal, single-level, multilevel, and population-based data.

Mplus is commonly used for advanced modeling approaches involving observed and latent variables, including factor analysis, structural equation modeling, mixture modeling, and simulation studies.

Key Uses

  • Analyze complex research datasets
  • Build statistical and latent variable models
  • Work with cross-sectional and longitudinal data
  • Analyze single-level and multilevel data
  • Model missing data and population differences
  • Conduct Monte Carlo simulation studies
  • Generate graphical displays of data and analysis results

Features

  • Support for continuous, categorical, count, and mixed variable types
  • Models for observed and unobserved population differences
  • Tools for analyzing missing data
  • Estimators and algorithms for advanced statistical modeling
  • Graphical displays of data and results
  • Monte Carlo simulation capabilities

Common Applications

  • Structural equation modeling
  • Factor analysis
  • Growth modeling
  • Multilevel modeling
  • Mixture modeling
  • Longitudinal data analysis
  • Social, behavioral, educational, and health research

E-Prime

E-Prime is a software platform for designing, running, and analyzing computerized research experiments. It is commonly used in psychology, neuroscience, education, and other research fields to collect participant responses, manage experimental workflows, and analyze study data.

E-Prime provides tools for building experiments, presenting stimuli, recording responses, and managing data collection without requiring extensive programming experience.

Key Uses

  • Design and conduct computerized research studies
  • Present surveys, tasks, and experimental stimuli
  • Collect participant response and performance data
  • Analyze and manage experimental results
  • Develop cognitive, behavioral, and educational research projects

Features

  • Visual experiment design environment
  • Tools for data collection and analysis
  • Support for touchscreen and tablet-based studies
  • Pre-built experiment templates and sample projects
  • Survey, scale, and multiple-choice question design tools
  • Script support for advanced customization
  • Experiment debugging and testing tools
  • Reusable templates and experiment libraries

What’s New in E-Prime 3

  • Updated interface with a tabbed workspace
  • Support for tablets and touchscreens
  • SlideButtons for response collection without scripting
  • Slide Layout Templates for faster experiment design
  • Downloadable experiment library and templates
  • Enhanced survey and scale design tools
  • Improved debugging, testing, and experiment management capabilities

Minitab

Minitab is a statistical analysis and data visualization platform that helps users analyze data, identify trends, evaluate relationships, and support data-driven decision-making. It provides tools for statistical analysis, graphing, predictive modeling, and quality improvement across academic, research, and operational environments.

Minitab is available as both a desktop application and a cloud-based web application, allowing users to access their work from multiple locations and devices.

Key Uses

  • Analyze and visualize data
  • Identify trends and patterns
  • Perform statistical analysis and hypothesis testing
  • Explore relationships between variables
  • Support research, quality improvement, and decision-making projects
  • Create charts, graphs, and reports to communicate findings

Features

  • Statistical analysis and modeling tools
  • Data visualization and graphing capabilities
  • Predictive analytics and trend analysis
  • Quality improvement and process analysis tools
  • Desktop and web-based access
  • Collaboration and data-sharing capabilities

Common Applications

  • Academic and research projects
  • Quality improvement initiatives
  • Process analysis and optimization
  • Engineering and scientific studies
  • Business and operational analytics
  • Data-driven decision making

GraphPad Prism

GraphPad Prism is a data analysis and graphing application designed for scientific and clinical research. It combines data organization, statistical analysis, curve fitting, and graph creation in a single platform, helping researchers analyze and present experimental results.

Prism provides tools for performing common statistical analyses and creating publication-ready graphs without requiring advanced statistical expertise.

Key Uses

  • Organize and manage research data
  • Create scientific graphs and data visualizations
  • Perform statistical analyses on experimental data
  • Conduct nonlinear regression and curve fitting
  • Prepare figures for publications, presentations, and reports
  • Analyze laboratory and clinical research results

Features

  • Scientific graphing and data visualization tools
  • Statistical analysis for common research workflows
  • Nonlinear regression and curve fitting
  • Data organization and management capabilities
  • Publication-ready chart and figure creation
  • Support for laboratory and clinical research applications

Common Statistical Analyses

  • t tests
  • Nonparametric tests
  • One-, two-, and three-way ANOVA
  • Contingency table analysis
  • Survival analysis

Additionally, BioRender Graphing is now available free of charge to University of Rochester Researchers, Faculty, Staff, and Students as part of the institutional BioRender offering. Graphing extends BioRender beyond scientific illustration by providing integrated tools for data visualization, statistical analysis, and figure creation within the same platform. Learn more about Graphing here.

 

Mathematica

Mathematica is a computational software platform used for mathematical modeling, data analysis, visualization, simulation, and scientific computing. It supports teaching, research, and problem-solving across a wide range of disciplines, including mathematics, engineering, physical sciences, life sciences, business, finance, and computer science.

Users can create interactive visualizations, perform complex calculations, develop simulations, and explore data within a single integrated environment.

Key Uses

  • Perform mathematical and scientific calculations
  • Create data visualizations and interactive models
  • Develop simulations and computational research projects
  • Analyze and explore research data
  • Support teaching and classroom demonstrations
  • Solve engineering, scientific, and business problems

Features

  • Mathematical computation and symbolic analysis
  • Data visualization and graphing tools
  • Modeling and simulation capabilities
  • Programming and automation tools
  • Statistical and data analysis functions
  • Interactive notebooks for research and instruction

SAS

SAS is a data management, statistical analysis, and reporting platform used for research, teaching, and advanced data analysis. It provides tools for data collection, management, statistical modeling, forecasting, reporting, and decision support across a wide range of academic and scientific disciplines.

SAS supports the complete data analysis workflow, from data preparation and visualization to statistical analysis and reporting.

Key Uses

  • Manage and analyze research data
  • Perform statistical and mathematical analyses
  • Create reports, charts, and data visualizations
  • Conduct forecasting and predictive analysis
  • Support research and academic projects
  • Develop data-driven models and applications
  • Analyze and improve processes and operations

Features

  • Data management and preparation tools
  • Statistical analysis and modeling
  • Data visualization and reporting
  • Forecasting and predictive analytics
  • Quality improvement and operations research tools
  • Application development capabilities
  • Decision support and business analytics tools

Common Applications

  • Academic and scientific research
  • Statistical analysis
  • Data management and reporting
  • Forecasting and predictive modeling
  • Quality improvement initiatives
  • Engineering and operational research
  • Teaching and instructional use

The University’s SAS license may be used for:

  • Academic instruction
  • Coursework and learning activities
  • Original academic research

Eligible users may install SAS on both University-owned and personally owned computers.

Licensing Restrictions

The University’s SAS license does not permit use for:

  • Institutional research
  • Administrative operations
  • University business functions
  • Healthcare operations or patient care
  • Sponsored research activities covered outside the academic license scope
  • Other non-academic operational uses

University staff who are participating in institutional research and other administrative uses can contact SAS.

SPSS / AMOS

SPSS Statistics is a data management and statistical analysis platform used by researchers, analysts, and students to organize, analyze, visualize, and report on data. It provides tools for the complete analytical workflow, from data preparation and management to statistical analysis and results reporting.

SPSS supports a wide range of research and data analysis projects and helps users generate tables, charts, and reports to communicate findings.

Key Uses

  • Manage and prepare datasets for analysis
  • Perform statistical analyses and hypothesis testing
  • Visualize data through charts and graphs
  • Generate tables, reports, and summaries
  • Conduct research and survey data analysis
  • Support academic, scientific, and operational research projects

Features

  • Data entry, preparation, and management tools
  • Statistical analysis and modeling capabilities
  • Data visualization and graphing tools
  • Reporting and output generation
  • Data import and export options
  • Tabular and graphical reporting features

Common Applications

  • Academic and scientific research
  • Survey and questionnaire analysis
  • Social and behavioral science research
  • Health and clinical research
  • Program evaluation
  • Business and operational analytics
  • Teaching and coursework

STATA

Stata is a statistical analysis, data management, and data visualization platform used for research, teaching, and analytical projects. It provides tools for managing datasets, performing statistical analyses, creating graphics, and reporting results within a single environment.

Stata supports a wide range of quantitative research methods and is commonly used across academic, scientific, health, social science, and business disciplines.

Key Uses

  • Manage and prepare research data
  • Perform statistical analyses and modeling
  • Create charts, graphs, and data visualizations
  • Conduct quantitative research and data analysis
  • Generate reports and analytical outputs
  • Support teaching, coursework, and research projects

Features

  • Data management and preparation tools
  • Statistical analysis and modeling capabilities
  • Data visualization and graphing tools
  • Reporting and output generation
  • Support for large and complex datasets
  • Integrated environment for data analysis workflows

Common Applications

  • Academic and scientific research
  • Health and clinical research
  • Social and behavioral sciences
  • Economics and public policy analysis
  • Survey and program evaluation
  • Teaching and coursework

MATLAB

MATLAB is a technical computing platform used for data analysis, mathematical modeling, simulation, visualization, and application development. It combines a programming environment with tools for working with data, creating models, and solving engineering, scientific, and research problems.

MATLAB is widely used in teaching, research, and development across disciplines, including engineering, mathematics, finance, life sciences, computer science, and medical research.

Key Uses

  • Analyze and visualize data
  • Develop mathematical and computational models
  • Create simulations and predictive models
  • Process signals, images, and other research data
  • Design and test control systems
  • Develop algorithms and technical applications
  • Support engineering, scientific, and research workflows

Features

  • High-level programming environment
  • Data analysis and visualization tools
  • Modeling and simulation capabilities
  • Signal and image processing tools
  • Machine learning and artificial intelligence toolboxes
  • Engineering and control system design tools
  • Integrated development and debugging environment

Common Applications

  • Engineering and scientific computing
  • Signal and image processing
  • Control system design
  • Data analysis and visualization
  • Financial modeling
  • Medical and biomedical research
  • Academic instruction and research

IMSL, PV-Wave

The IMSL Fortran, C/C++ and C# Numerical Libraries provide pre-written mathematical and statistical algorithms that can be imbedded into Fortran, C/C++, C# for .NET, and Java applications. JMSL for Java applications combines integrated charting with IMSL Library algorithms. PV-WAVE is an array-oriented programming language used to build and deploy visual data analysis applications. TS-WAVE is for advanced time series analysis, and JWAVE’s multiple graphical and visualization techniques allows for web-based visual data analysis across platforms.

 

IMSL provides mathematical, statistical, and data analysis libraries that help developers add advanced analytical capabilities to software applications. The libraries include pre-built algorithms that can be incorporated into applications written in Fortran, C, C++, C#, .NET, and Java.

In addition to the IMSL libraries, the platform includes tools for scientific computing, data visualization, and time series analysis.

Key Uses

  • Add mathematical and statistical functionality to software applications
  • Perform numerical analysis and scientific computing
  • Create data visualizations and charts
  • Conduct time series analysis
  • Develop custom research and data analysis applications
  • Support engineering, scientific, and research computing workflows

Included Components

IMSL Numerical Libraries

Pre-built mathematical and statistical algorithms for Fortran, C, C++, C#, .NET, and Java applications.

JMSL for Java

A Java-based library that combines mathematical and statistical algorithms with integrated charting and visualization capabilities.

PV-WAVE

A programming environment for developing scientific, engineering, and data visualization applications.

TS-WAVE

A toolkit for time series analysis and forecasting.

JWAVE

A web-based data visualization platform that provides graphical and analytical tools for exploring and presenting data across multiple platforms.

Common Applications

  • Scientific and engineering research
  • Statistical modeling and analysis
  • Application development
  • Data visualization and reporting
  • Time series analysis and forecasting
  • Computational research projects

JMP Student Edition

JMP Student Edition is a data analysis and statistical software package that helps users explore, visualize, and analyze data. It provides tools for statistical analysis, data visualization, experimental design, predictive modeling, and machine learning, making it suitable for coursework, research, and data-driven projects.

JMP supports an interactive approach to data analysis, helping users identify patterns, evaluate results, and communicate findings through charts, graphs, and reports.

Key Uses

  • Explore and visualize data
  • Perform statistical analyses
  • Design and analyze experiments
  • Build predictive and statistical models
  • Conduct machine learning and data mining projects
  • Integrate Python into data analysis workflows
  • Communicate results through charts and reports

Features

  • Interactive data visualization tools
  • Statistical analysis and modeling
  • Design of experiments (DOE) capabilities
  • Machine learning workflows
  • Predictive analytics tools
  • Python integration
  • Reporting and data presentation features

Common Applications

  • Academic coursework
  • Research and data analysis
  • Experimental design
  • Statistical modeling
  • Engineering and scientific studies
  • Business and operational analytics