Exercise

# In-sample RMSE for linear regression on diamonds

As you saw in the video, included in the course is the `diamonds`

dataset, which is a classic dataset from the `ggplot2`

package. The dataset contains physical attributes of diamonds as well as the price they sold for. One interesting modeling challenge is predicting diamond price based on their attributes using something like a linear regression.

Recall that to fit a linear regression, you use the `lm()`

function in the following format:

```
mod <- lm(y ~ x, my_data)
```

To make predictions using `mod`

on the original data, you call the `predict()`

function:

```
pred <- predict(mod, my_data)
```

Instructions

**100 XP**

- Fit a linear model on the
`diamonds`

dataset predicting`price`

using all other variables as predictors (i.e.`price ~ .`

). Save the result to`model`

. - Make predictions using
`model`

on the full original dataset and save the result to`p`

. - Compute errors using the formula \(errors = predicted - actual\). Save the result to
`error`

. - Compute RMSE using the formula you learned in the video and print it to the console.