An official for a national dog show studies the characteristics of one breed of dog, the Dandie Dinmont Terrier. Two common measurements are the height and weight of the dog, and the official would like to develop a model that would be helpful in predicting weight based on a given height. The official first makes a scatterplot that relates height and weight, then another that compares the logs of each measurement. A graph titled Dandie Dinmont Terrier height versus weight has height (inches) on the x-axis, and weight (pounds) on the y-axis. The points curve up.

A graph titled Dandie Dinmont Terrier log height versus log weight has log (height) on the x-axis, and log (weight) on the y-axis. The points curve up.

Based on the graphs, which type of model is likely appropriate for predicting weight from height?

A linear model is appropriate because the graph of the transformed data is roughly linear.
A power model is appropriate because the scatterplot of height versus weight appears curved.
A power model could be appropriate because the scatterplot of log height versus log weight is roughly linear. The next step is to look at the residual plot.
An exponential model is appropriate because the scatterplot of log height versus log weight has a stronger linear relationship than the scatterplot of the non-transformed data.