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  3. Handling Missing Data with Imputations in R

Exercise

Linear regression with incomplete data

Missing data is a common problem and dealing with it appropriately is extremely important. Ignoring the missing data points or filling them incorrectly may cause the models to work in unexpected ways and cause the predictions and inferences to be biased.

In this chapter, you will be working with the biopics dataset. It contains information on a number of biographical movies, including their earnings, subject characteristics and some other variables. Some of the data points are, however, missing. The original data comes with the fivethirtyeight R package, but in this course, you will work with a slightly preprocessed version.

In this exercise, you will get to know the dataset and fit a linear regression model to explain a movie's earnings. Let's begin!

Instructions 1/4

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  • Print the first 10 observations of the biopics data and get familiar with the variables.