# Question 4 - log-linear model. # CG Q4a # Now, run a linear regression using log ride counts as the response ###### modeled by the weather variables weathersit, temp, hum, and windspeed. ###### Use these same predictors as ridefit and name this new model logridefit # CG Q4b # Use summary() on your fitted model to print the results. # CG Q4c # How does expected log ride count change with an increase in temperature? ####### Print the coefficient for temp in the logridefit model ####### using the same strategy used in Q2c. # CG Q4d # Based on the temp coefficient from log ridefit, ####### do we expect ride count to increase ####### or decrease with an increase in temperature? ####### If log ride count is expected to increae with temperature, ####### type paste("increase"), otherwise paste("decrease"). # CG Q4e # Now print the multiplicative effect of temperature #######_on_expected ride count by wrapping the line of code ####### from Q4c in the exp() function. # CG Q4f # Find the R-squared for the logridefit regression. ####### In your calculation, use logridefit$deviance and ####### logridefit$null.deviance and not the numbers printed ####### in the summary output for these. # Question 5 log-log model # CG Q5a # Now, run a linear regression using log ride counts as the response ###### modeled by the weather variables weathersit, log(temp), hum, and windspeed. ###### Note the log() on the temp predictor. ###### Name your model loglogfit. # CG Q5b # Use summary() on your fitted model to print the results.