Examining & interpreting logistic regression outputs

In the previous exercise, you fit a logistic regression, bus_out. During this exercise, you will examine bus_out and interpret the results of the regression using the tools you learned about in Chapter 1:

• print() includes the coefficient estimates (i.e., slopes and intercepts) for different predictor variables and information about the model fit such as deviance.
• summary() includes the print() outputs as well as standard errors, z-scores, and P-values for the coefficient estimates.
• tidy() includes the summary() coefficient table as a tidy data frame.

Recall that regression coefficients can help us understand both the direction of relationships and statistical significance of coefficients. For logistic regression, a positive coefficient indicates that the probability of an event occurring increases as a predictor increases.