Regression

Predicts one outcome from one or more predictors, with optional moderation and mediation. Three model types are available: linear (OLS) for a numeric outcome, binary logistic for a two-category outcome, and multinomial logistic for an unordered outcome with three or more categories.

When to use it

To model how an outcome depends on several variables at once — for example, predicting a test score from age and income — and to estimate each predictor’s unique contribution.

Inputs

  • Dependent — the outcome: numeric for linear, two categories for binary logistic, or three-plus categories for multinomial logistic.

  • Independent — one or more predictors.

  • Moderator (optional) — adds an interaction (moderation model).

  • Mediator (optional) — fits a mediation model with indirect paths.

Moderation and mediation are mutually exclusive.

Options

  • Model — Linear (OLS), Logistic (binary), or Multinomial (logistic).

  • Standardised coefficients — reports standardised (β) alongside unstandardised estimates (linear model).

  • Diagnostics — an influence table (Mahalanobis distance, Cook’s distance, leverage, studentized residuals, flagging the observations that exceed the usual cut-offs) plus the Durbin–Watson autocorrelation statistic and residual plots. This is a report only — nothing is excluded from the model.

  • Verbal indicators (in-table columns), Verbal report (dropdown for how much written interpretation: None / Key findings / Significant only / Full), and Plots.

Output

  • A model fit table (R², adjusted R², F, p for OLS; pseudo-R² and a likelihood-ratio χ² for the logistic models).

  • A coefficients table (estimates, standard errors, t/z, p, CIs; standardised if requested; odds ratios for logistic). The multinomial model reports one coefficient block per non-reference category, each compared against the first category as the baseline.

  • Path tables for mediation (plus an X → M → Y path diagram for a single-predictor mediation when plots are on — with arrow-colour, label-size and spread controls), diagnostics when enabled, and a plot.

  • Plots. With a single predictor the plot is a scatter with the fitted line (plus simple slopes / mediation paths where relevant). With several predictors there is no 2-D scatter, so an observed-vs-predicted plot is drawn instead (each point is a case, plotted as its actual outcome against the model’s prediction; points near the 45° line indicate a good fit).

Using categorical predictors

Regression accepts only numeric / ordinal predictors. To include a nominal variable (e.g. region), first convert it with One-hot encoding (see One-hot encoding); the resulting 0/1 indicator columns can then be selected as predictors.

Notes

  • Rows with any missing value in the used columns are dropped (list-wise).

  • A predictor literally named const is not allowed (the model adds its own intercept).