Common problems with complex modeling analysis with R is that model results are often complex objects and getting to values, like model coefficients, demand a lot of manipulations; others vary from one model to another. Fortunately, the `broom`

package provides nice and easy to use solutions to the problem.

Answers to the exercises are available here.

Please do all exercises using the `tidiverse`

packages (mostly `broom`

and `ggplot2`

.) If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.

**Exercise 1**

The plot shows displacement vs. miles per gallon for the `mtcars`

data set. Fit the data with the linear model and display the model’s coefficients as a tidy `data.frame`

.

**Exercise 2**

Reconstruct the plot from exercise 1, adding a prediction line.

**Exercise 3**

Fit the data with inverse relations instead of a linear one. Like before, display the coefficients and add a prediction line to the plot. (Hint: use `nls`

function)

**Exercise 4**

Compare the performance of both models.

**Exercise 5**

To evaluate the model stability, re-fit the better of the models 100 times with the bootstrap procedure and plot histograms of coefficients.

**Exercise 6**

Plot the predictions from bootstrapped models.

**Exercise 7**

Fit the model once again, but this time, separately for a different number of cylinders (`cyl`

variable). Plot the prediction for each group separately, plus the prediction from the previous model.

**Exercise 8**

Display the model performance summaries. Compare the sum of deviance with a model not grouped by the number of cylinders.

**Exercise 9**

Apply k-means on the `iris`

data set testing `k`

from 2 to 5. Display the performance summary for each number of clusters.

**Exercise 10**

Plot the `Petal.Length`

vs. `Petal.Width`

showing assignment to clusters and centers of the cluster.

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