Exercise

# Linear regression with principal components

The object `newsData`

now contains an additional variable: `logShares`

. The number of shares tell you how often the news articles have been shared. This distribution, however, would be highly skewed, so you are going to work with the logarithm of the number of shares. Apply what you just learned and predict the log shares!

Instructions

**100 XP**

- Compute a model to predict the log shares with all other variables. Store it as
`mod1`

. - Create a new dataframe
`dataNewsComponents`

with the log shares and the values on the first 6 components. The object`pcaNews`

again contains the PCA results. - Compute a second model (
`mod2`

) that predicts the log shares with just the 6 components. - Compare the adjusted R squared of the models. How did the value change by using only the principal components? How good is your model?