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 theprint()
outputs as well as standard errors, z-scores, and P-values for the coefficient estimates.tidy()
includes thesummary()
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.
This exercise is part of the course
Generalized Linear Models in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print the bus_out with the print() function
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