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Running glm explorer — the right regression family, chosen from your data analysis...
Sent to — interactive charts, statistical results, R code, and AI insights.
Analyze another fileStandard-library analysis: regression that inspects your outcome column and chooses the correct model family instead of assuming one — what it adds over plain linear regression. A yes/no outcome gets logistic regression with odds ratios; a non-negative whole-number count gets Poisson regression with rate ratios (upgraded to quasi-Poisson when an overdispersion test demands it); a continuous outcome gets ordinary least squares with plain coefficients. The family decision and its evidence are reported openly, every effect carries a 95% confidence interval and plain-language significance, and you get the strongest predictor's partial-effect curve plus a predicted-vs-actual fit check (binned calibration for logistic). Works on any dataset: map one outcome and 1-8 predictors.
Interactive horizontal_bar visualization
Interactive scatter visualization
Interactive scatter visualization
Interactive table visualization
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Explain churn (yes/no) with odds ratios instead of a misfit straight line
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — validated R analyses, interactive reports, AI insights, and PDF export.
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