Binary logistic regression

MinutePlot^® and R on the same data. MinutePlot version 1.0.0; R 4.5.2.

Dataset

• mtcars (am, wt, hp) — 32 cars from the 1974 Motor Trend road tests: transmission (am: 0 automatic, 1 manual), weight (wt, 1000 lb) and gross horsepower (hp) (R: datasets::mtcars).
• Data file (the same format serves MinutePlot and R): mtcars_am_wt_hp.csv — one column per variable; paste it into the app, and keep it in the R script’s folder.
• R built-in: the script uses mtcars.

Setup

In MinutePlot:

• Workbench → Binary logistic regression → paste mtcars_am_wt_hp.csv → Preview data → tick first column is sample names (the car column) → outcome column am, predictors wt and hp → Confirm → Fit logistic model.

In R:

• Base R only; then run mtcars_logistic.R top to bottom (source it, or paste it into the console) with the R data file in the same folder.
• R 4.5.2 and the package versions in the page header; each script prints the versions it runs under.

The analysis:

• Binary logistic regression of am on wt and hp (maximum likelihood, MinutePlot’s iteratively reweighted least squares). R reference: glm(am ~ wt + hp, family = binomial).
• Coefficients on the log-odds scale with standard errors and two-sided Wald p-values; the fit summary as MinutePlot reports it: residual deviance (−2 log-likelihood), null deviance, AIC, log-likelihood, McFadden’s pseudo-R² and the likelihood-ratio test of the model.

Side-by-side results

Coefficients (log-odds)

Fit summary

Verdict

R script

mtcars_logistic.R — each script loads its data, prints its versions and the R-side tables shown above, in page order; no plotting.