The summary of the logistic regression model produced with the glm() function has a similar format to that of the linear regression model produced with the lm() function. This shows us that for our categorical variables, we have one fewer binary feature than the number of levels in the original variable, so for example, the three-valued THAL input feature produced two binary variables labeled THAL6 and THAL7. We'll begin by looking first at the regression coefficients that are predicted with our model. These are presented with their corresponding z-statistic. This is analogous to the t-statistic that we saw in linear regression, and again, the higher the absolute value of the z-statistic, the more likely it is that this particular feature is significantly related to our output variable. The p-values next to the z-statistic express this notion as a probability and are annotated with stars and dots, as they were in linear regression, indicating the smallest...