Transformed model
As you saw in the previous chapter, transforming the variables can often transform a model from one where the technical conditions are violated to one where the technical conditions hold. When technical conditions hold, you are able to accurately interpret the inferential output. In the two models below, note how the standard errors and p-values change (although in both settings the p-value is significant).
This exercise is part of the course
Inference for Linear Regression in R
Exercise instructions
- Run a linear regression on
priceversusbedfor theLAhomesdataset, then tidy the output. - Do the same on log-transformed variables:
log(price)versuslog(bed).
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a tidy model
# Create a tidy model using the log of both variables