Calculate RMSE

In this exercise you will calculate the RMSE of your unemployment model. In the previous coding exercises, you added two columns to the unemployment dataset:

the model's predictions (predictions column)
the residuals between the predictions and the outcome (residuals column)

You can calculate the RMSE from a vector of residuals, $res$, as:

$$ RMSE = \sqrt{\operatorname{mean}(res^2)} $$

You want RMSE to be small. How small is "small"? One heuristic is to compare the RMSE to the standard deviation of the outcome. With a good model, the RMSE should be smaller.

The data frame unemployment is in your workspace.

Review the unemployment data from the previous exercise.
For convenience, assign the residuals column from unemployment to the variable res.
Calculate RMSE: square res, take its mean, and then square root it. Assign this to the variable rmse and print it.
- Tip: you can do this in one step by wrapping the assignment in parentheses: (rmse <- ___)
Calculate the standard deviation of female_unemployment and assign it to the variable sd_unemployment. Print it. How does the rmse of the model compare to the standard deviation of the data?

Take Hint (-30 XP)

Exercise

Calculate RMSE

Instructions