Once the six predictors make sense, the next step is to test them: challenge this model in the Model Builder — add a variable the published model left out, drop one it kept, and see what changes.
What is the published model?
A model combines several survey answers at once to describe how they are associated with the headline outcome. The six variables below make up the published reference model used in this case study. They are a disclosed starting point, not a claim that this is the uniquely best or optimized specification. The Model Builder lets you add, remove, and compare variables to see whether another specification fits the same released evidence better.
Loading model and microdata…
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Every dot above is one prediction. To see where those numbers come from, pull one case out of the cloud — pick the highest, the lowest, a specific one, or leave it on a random draw:
The featured one has been pulled out of the cloud. Here is what they told the interviewer:
Their answers
These six answers are everything the model knows about this respondent.
How the model thinks about this respondent
The model runs those inputs through an equation. Each non-reference input has a coefficient — a number, learned when the model was fit on all — respondents, that says how much this input pushes the predicted probability up or down. Reference inputs contribute zero; the constant absorbs them. Continuous predictors contribute their coefficient times the measured value.
The calculation for the featured one, row by row:
The total at the bottom is on the log-odds scale. The logistic function converts that total into a probability between 0 and 1 (for readers who want the arithmetic, p = 1 / (1 + e−x)):
What actually happened?
The featured one's actual outcome is on record. The prediction above was computed from the recorded inputs and fitted coefficients, not from this case's observed outcome, so the model-implied probability and the actual result can sit side by side:
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What if this one had different inputs?
The dropdowns and sliders below start at this one's actual values. Change any to a different value, then click Apply. The probability curve below recomputes the prediction for this one only — the rest of the sample stays untouched. The locked baseline at the top of the page stays where it is, so you can compare side by side.
They're one of —
The featured one's prediction is one number. The model runs the same calculation for every respondent in the sample, producing — predicted probabilities. Plotted together:
Each bar is a 5-percentage-point bin. The vertical line marks the weighted mean of all those probabilities — the headline:
The featured one sits at —. Plenty of respondents sit far above or far below the headline — the spread on either side is what the headline hides.
How well does the model sort people like the featured one?
That last section compared the featured one's prediction against what actually happened for that one respondent. The same comparison works across the whole sample, because the actual outcome is known for every respondent. The model assigns each case a predicted probability from its recorded inputs and the fitted coefficients. So the question can be asked after the fact: did the model give higher predicted probabilities to actual approvers and lower ones to actual non-approvers?
The same cloud, split by what actually happened:
Actual approvers got an average predicted probability of —; actual non-approvers averaged —. The gap between those means is Tjur's R², in probability units (shown here as percentage points):
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0 pp means no separation — the predicted probability tells you nothing about which group the respondent belongs to. 100 pp means perfect separation. Real-world models land in between.
This number measures how well the published model separates the two groups on the data it was fit on, and it stays fixed through the scenarios below. (An all-or-nothing scenario homogenizes one predictor across every respondent, which mechanically shrinks the gap and falsely suggests the model "got worse" under the scenario — but nothing about the model has changed.)
And what if everyone had answered differently?
Earlier you watched the featured case's prediction shift when you changed one of its inputs. Only that one moved — the cloud and the headline stayed put because the rest of the sample didn't change. The simulator below applies the same kind of swap to everyone: pin one or more inputs to a single value, and every case in the sample gets that value. The headline tiles and the cloud below redraw to show the shift.
This is a teaching version of the population simulator. For the full release-day scenario tool, use the All-or-Nothing page.