Key Results: P-Value, Coefficients
In these results, all three predictors are statistically significant at the 0.05 level. You can conclude that changes in these variables are associated with changes in the response variable.
Use the coefficient to determine whether a change in a predictor variable makes the event more likely or less likely. The estimated coefficient for a predictor represents the change in the link function for each unit change in the predictor, while the other predictors in the model are held constant. The relationship between the coefficient and the number of events depends on several aspects of the analysis, including the link function and the reference levels for categorical predictors that are in the model. Generally, positive coefficients make the event more likely and negative coefficients make the event less likely. An estimated coefficient near zero implies that the effect of the predictor is small, or nonexistent.
The interpretation of the estimated coefficients for categorical predictors is relative to the reference level of the predictor. Positive coefficients indicate that the event is more likely at that level of the predictor than at the reference level of the factor. Negative coefficients indicate that the event is less likely at that level of the predictor than at the reference level.
The coefficient for Hours Since Cleanse is positive, which suggests that longer hours are associated with higher values of the response. The coefficient for temperature is negative, which suggests that higher temperatures are associated with lower values of the response.
The size of the screw is a categorical variable with one coefficient, which indicates that the variable has 2 levels and the variable uses 0, 1 coding. The coefficient for the small screw is negative, so the small screw is associated with lower values of the response than the reference level.
If an interaction term is statistically significant, the relationship between a predictor and the response differs by the level of the other predictor. In this case, you should not interpret the main effects without considering the interaction effect. To obtain a better understanding of the main effects, interaction effects, and curvature in your model, go to Factorial Plots and Response Optimizer.