# Question $6-$ Let's practice making predictions of the response variable.

# CG Q$6$a # We'll make predictions using all $3$ competing models for

####### a wet day that is $25$ degrees, has $50\%$ humidity and windspeed of $5.$

####### Make a data frame of these values and call it newdata.

newdatadata.frame$($weathersit$=2,$temp$=25,$hum$=50,$windspeed$=5)$

# CG Q$6$b # Now use ridefit model to predict ride count using

####### the predict$()$ function with your newdata object.

predict$($ridefit$,$newdata$)$

# CG Q$6$c # Now use the logridefit model to predict log ride count using

####### the predict$()$ function with your newdata object.

predict$($logridefit$,$newdata$=$newdata$)$

# CG Q$6$d # To put the prediction of the log ride count back onto the raw

####### ride count scale, wrap the line of code from Q$6$c in the exp$()$

####### function to "undo" the log transformation.

exp$($predict$($logridefit$,$newdata$=$newdata$))$

# CG Q$6$e # use the loglogfit model to predict log ride count using

####### the predict$()$ function with your newdata object.

predict$($loglogfit$,$newdata$=$bikes$)$

# CG Q$6$f # To put the prediction of the log ride count back onto the raw

####### ride count scale, wrap the line of code from Q$6$e in the exp$()$

####### function to "undo" the log transformation.