Regression Analysis
Regression analysis is an important tool in Biostat Prime, enabling users to model and quantify relationships between variables—key for data-driven decision making in food science, health research, and beyond. Whether you're predicting nutrient absorption rates, modeling biomarker responses, or investigating diet–disease associations, Biostat Prime offers both linear and nonlinear regression capabilities to fit virtually any analytical requirement.
Regression Analysis in Biostat Prime
Regression analysis is one of the most powerful statistical tools in biostatistics and life sciences. It allows researchers and analysts to examine the relationships between variables—especially how an outcome (dependent variable) is influenced by one or more predictors (independent variables). In Biostat Prime, regression analysis is fully integrated into the analytical workflow, giving users an intuitive and flexible environment for both linear and nonlinear regression models.
Types of Regression in Biostat Prime
Linear regression
Linear regression assumes a straight-line relationship between the dependent variable and the predictors.
Linear Regression module supports simple and multiple regression
Residual analysis with plots and normality checks
Export-ready tables and graphs for publication or reporting
Non Linear regression
When relationships between variables are curved or saturating, nonlinear regression is required.
BioStat Prime Supports Dedicated Nonlinear Regression module, Predefined models, User-defined custom equations, Smart parameter estimation, Graphical overlays of predicted vs. observed data, Residual plots and convergence diagnostics