Interpret the key results for Binary Logistic Regression - Minitab Express

Interpret the key results for Binary Logistic Regression. In This Topic. Step 1: Determine whether the association between the response and the term is statistically significant. Key Result: P-Value. In these results, the dosage is statistically significant at the significance level of 0.05. You can conclude that changes in the dosage are associated with changes in the probability that the event occurs. Assess the coefficient to determine whether a change in a predictor variable makes the event more likely or less likely. The relationship between the coefficient and the probability depends on several aspects of the analysis, including the link function. Generally, positive coefficients indicate that the event becomes more likely as the predictor increases. Negative coefficients indicate that the event becomes less likely as the predictor increases. For more information, go to Coefficients and Regression equation. The coefficient for Dose is 3.63, which suggests that higher dosages are associated with higher probabilities that the event will occur. Step 2: Understand the effects of the predictors. Key Result: Odds Ratio. In these results, the model uses the dosage level of a medicine to predict the presence or absence of bacteria in adults. The odds ratio indicates that for every 1 mg increase in the dosage level, the likelihood that no bacteria is present increases by approximately 38 times. Key Result: Odds Ratio. In these results, the response indicates whether a consumer bought a cereal and the categorical predictor indicates whether the consumer saw an advertisement about that cereal. The odds ratio is 3.06, which indicates that the odds that a consumer buys the cereal is 3 times higher for consumers who viewed the advertisement compared to consumers who didn't view the advertisement. Step 3: Determine how well the model fits your data. To determine how well the model fits your data, examine the statistics in the Model Summary table. For binary logistic regression, the data format affects the deviance R 2 statistics but not the AIC. For more information, go to For more information, go to How data formats affect goodness-of-fit in binary logistic regression. The higher the deviance R 2 , the better the model fits your data. Deviance R 2 is always between 0% and 100%. Deviance R 2 always increases when you add additional predictors to a model. For example, the best 5-predictor model will always have an R 2 that is at least as high as the best 4-predictor model. Therefore, deviance R 2 is most useful when you compare models of the same size. For binary logistic regression, the format of the data affects the deviance R 2 value. The deviance R 2 is usually higher for data in Event/Trial format. Deviance R 2 values are comparable only between models that use the same data format. Deviance R 2 is just one measure of how well the model fits the data. Even when a model has a high R 2 , you should check the residual plots to assess how well the model fits the data. Deviance R-sq (adj) Use adjusted deviance R 2 to compare models that have different numbers of predictors. Deviance R 2 always increases when you add a predictor to the model. The adjusted deviance R 2 value incorporates the number of predictors in the model to help you choose the correct model. AIC Use AIC to compare different models. The smaller the AIC, the better the model fits the data. However, the model with the smallest AIC does not necessarily fit the data well. Also use the residual plots to assess how well the model fits the data.