Exercise

# Confidence intervals on log-transformed models

The previous exercise highlighted that the model output for a log-transformed response is in terms of the logarithm of the response variable.

The effect size for a log-transformed value is in terms of *change of logarithm* per unit of the explanatory variable. It's generally easier to interpret this as a percentage change per unit of the explanatory variable, which also involves an exponential transformation: `100 * (exp(__effect_size__) - 1)`

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Instructions

**100 XP**

- Build the model of log-transformed price as shown in the editor.
- Create an ensemble of 100 bootstrap replications of the model.
- Calculate the effect size
`~ Age`

on the bootstrap replications. - Transform the numerical value of the
`slope`

to a*percentage change per unit of the explanatory variable*. - Find the 95% confidence interval on the percentage change per year of age. You can use the usual method: mean plus-or-minus twice the standard deviation.