Top 10 Best Multiple Regression Software of 2026

Python enables multiple regression work with interchangeable modules that separate estimation, inference, and evaluation. Statsmodels covers ordinary least squares and regression diagnostics such as residual plots and heteroscedasticity tests, plus summary outputs for coefficient inference. Scikit-learn handles ridge and lasso regularization workflows with cross-validation and practical pipelines. Data ingestion is typically script-driven through CSV or Parquet readers, and model reproducibility is maintained via versioned code and serialized artifacts.

A key tradeoff is that the core language needs add-on libraries for inference-grade reporting, so teams must standardize library choices and preprocessing steps. Python fits situations where analysts must automate model fitting across many datasets, versions, or feature sets. It also fits environments that require programmatic prediction scoring and batch inference with consistent preprocessing between training and serving.

SAS handles multiple regression through its linear modeling procedures that compute coefficient estimates, fitted values, and common significance tests. Regression diagnostics such as influence measures and residual plots are built into standard model workflows, which reduces manual glue code. Automation is strong through SAS program execution, scheduled runs, and parameterized model scripts that can produce consistent outputs across datasets. Integration depth is also a differentiator since SAS can read common enterprise statistical formats and run scoring in the same analytics runtime used for training.

A key tradeoff is that interactive modeling is less lightweight than R or Python, because many workflows center on SAS program structure and enterprise run patterns. SAS fits best when organizations need repeatable regression pipelines with controlled execution, model artifacts that can be rerun on new data, and standardized diagnostic reporting for reviewers.

Prism’s multiple regression workflow centers on interactive dataset management, where each analysis is tied to the workbook’s data tables and can be re-run after edits. The model output includes parameter estimates, confidence intervals, and visual diagnostics such as residual and Q-Q plots for regression diagnostics. GraphPad also provides tools for handling categorical predictors via a workflow built around grouping and term specification inside the GUI. Export options support moving results into reports, but the workflow stays oriented around manual review rather than automated batch fitting.

A key tradeoff is limited automation depth for regression pipelines, since Prism’s interaction model does not prioritize an external programmatic API for model training, scoring, and serialization at scale. Prism also focuses on common regression use cases and does not aim to match the breadth of R or SAS for custom modeling families and extensive inference options. Prism fits best when analysts repeatedly revise a few candidate models, validate assumptions visually, and need consistent figure generation for manuscripts or internal reports.

SPSS focuses on menu-driven multiple regression workflows that produce publishable statistical output without requiring code. Multiple regression setup covers linear predictors, dummy coding for categorical inputs, and standard diagnostics such as multicollinearity checks and influence measures.

SPSS also supports automation for repeatable analyses through saved syntax and batch execution, which matters for model refresh across many datasets. Data handling is centered on SPSS file workflows, with import and export paths for common statistical formats used in analyst-to-analyst handoffs.

NCSS performs multiple regression fitting with OLS and a workflow for model diagnostics and coefficient interpretation. The package focuses on analyst-driven, menu-led specification and output generation, with batchable script-style runs for repeated modeling.

NCSS includes diagnostics such as residual plots and specification tests aimed at checking key regression assumptions. Export paths support coefficient tables and prediction outputs for downstream reporting and scoring.

MATLAB supports multiple regression workflows through functions in Statistics and Machine Learning Toolbox, with both linear model fitting and diagnostic outputs in one environment. The workspace-centric data model, formula-based model specification, and tight plotting integration make it practical for analysts who iterate on residual checks, influence metrics, and model refinement.

Automation is available through MATLAB scripting, batch fitting, and model object serialization for repeatable prediction scoring. Compared with R and Python, MATLAB’s strength is end-to-end modeling plus visualization and deployment-oriented packaging within the same runtime.

jamovi combines a spreadsheet-like GUI with an R-backed engine for ordinary least squares and regression-style workflows. Multiple regression runs from the interface with diagnostic plots and assumption checks that update as variables change.

The software also supports scriptable output so analyses can be repeated outside a click-only workflow. Export of model results and coefficients supports downstream reporting and scoring in other tools.

gretl is a statistical modeling environment that centers multiple regression through scripted workflows and reproducible analysis runs. It provides estimation routines for ordinary least squares, fixed-effect specifications, and common inference output like coefficient tests and diagnostic plots.

The software also supports data import from common desktop formats and SPSS portable exports, plus batch execution via its command language. Compared with general-purpose stacks, gretl offers a tighter regression-centered toolchain with fewer moving parts for analysts who script models.

Microsoft Excel performs multiple regression by generating coefficient estimates and statistical tests from worksheet ranges using the built-in Data Analysis tools and worksheet functions. Excel handles ordinary least squares workflows with diagnostics like residual and leverage inspection, plus options for adding categorical terms through dummy coding.

Model building can be automated by recording macros and driving Excel through external integrations that read and write workbook data via programmatic file handling and scripting. Excel is a practical choice for analysts who need iterative modeling inside a spreadsheet interface and then export coefficients for downstream scoring or reporting.

JASP is a GUI-first multiple regression tool aimed at analysts who want model building, diagnostics, and reporting without writing code. Its core workflow covers ordinary least squares with familiar outputs like coefficients, confidence intervals, residual plots, and model fit summaries for multiple predictors.

JASP also supports generalized linear model use cases, so the same interface can cover more than plain regression when the dependent variable is not continuous. Exports are built for reproducible writeups, with results that map cleanly into papers and slides rather than only interactive exploration.