Module 10 Activity

Scenario

Model a binary outcome and report it in probabilities a reader can use.

What you build

A logistic regression with odds ratios, predicted probabilities and a justified threshold.

Steps

1. Confirm the outcome is binary and check what a linear model would predict at the extremes of your predictor.
2. Fit with glm(..., family = binomial) and verify the output reports deviance rather than a residual standard error.
3. Report odds ratios with intervals, label them as odds ratios, and give the baseline rate.
4. Produce a table of predicted probabilities at meaningful predictor values, with intervals computed on the link scale.
5. Report the confusion matrix against the majority-class baseline, and say how many positives were caught.
6. Check calibration by binning predictions against observed rates, then choose a threshold from the relative cost of misses and false alarms.

Evidence to hand in

• What a linear model predicted, and where it went out of range.
• The fitted model with its deviance.
• The odds-ratio table with the baseline rate.
• The predicted-probability table with intervals.
• The confusion matrix against the majority baseline.
• The calibration check and your threshold justification.

Review checklist

• family = binomial is present.
• Odds ratios are labelled as such and the baseline rate is given.
• Predicted-probability intervals were built on the link scale.
• Accuracy is compared to the majority-class rate, not to 50%.
• The threshold is justified by costs, not left at 0.5 by default.

Mark complete