Exercise

# Interpreting transformed coefficients

Transforming variables is a powerful tool to use when running linear regressions. However the parameter estimates must be carefully interpreted in a model with transformed variables.

Consider data collected by Andrew Bray at Reed College on characteristics of LA Homes in 2010. The model is given below, and your task is to provide the appropriate interpretation of the coefficient on `log(sqft)`

?

Note: you must be careful to avoid causative interpretations. Additional square footage does not necessarily cause the price of a specific house to go up. The interpretation of the coefficient describes the estimate of the average price of homes at a given square footage.

You will need to run the linear model before answering the question:

`lm(log(price) ~ log(sqft), data = LAhomes) %>% tidy()`

Instructions

**50 XP**

##### Possible Answers

- Each additional square foot of house size produces an estimate of the average price which is $1.44 more.
- Each additional square foot of house size produces an estimate of the average price which is $1,442 more.
- Each additional square foot of house size produces an estimate of the average price which is 1.44% higher.
- Each additional 1% of square footage produces an estimate of the average price which is $1.44 more.
- Each additional 1% of square footage produces an estimate of the average price which is 1.44% higher.