Model to Meaning: How to Interpret Statistical Models with R and Python by Arel-Bundock Vincent

Author:Arel-Bundock, Vincent
Language: eng
Format: epub, pdf
Publisher: CRC Press
Published: 2025-08-06T00:00:00+00:00

Page 117The difference between our two estimates is about -0.039, which suggests that the slope is flatter in the group with incentive. In other words, the association between distance and outcome is weaker when incentive=1 than when incentive=0. However, this difference is not statistically significant (p=0.142). Therefore, we cannot reject the null hypothesis of homogeneity in the effect of distance on the predicted probability that participants will seek their test results.

7.6 Visualization

When interpreting the results obtained by fitting a model, it is important to keep in mind the distinction between predictions and slopes. Predictions give us the level of an expected outcome, for given values of the predictors. Slopes capture how the expected outcome changes in response to a change in a focal variable, while holding adjustment variables constant. Figure 7.3 illustrates these concepts in two panels.

Long Description for Figure 7.3: The figure includes two vertically arranged line graphs. The top graph displays the predicted probability of the outcome being 1 along the vertical axis and distance along the horizontal axis. The curve starts high near 0.9 and gently declines as distance increases, with a slight flattening near the end. A shaded region surrounds the curve, indicating uncertainty or confidence intervals. The bottom graph presents the slope or derivative of the top graph's curve, labelled d Y over d X on the vertical axis and distance on the horizontal axis. The slope starts negative, becomes less steep around distance 3, turns slightly positive briefly, and then decreases again towards the end. The shaded area around the slope line widens at higher distances, suggesting greater uncertainty.

Figure 7.3: Model-based predictions (top) and slopes (bottom).⏎

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