Module 1: Tutorial: Regression

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Linear Regression

Description of data set

The file module1-video_reading.csv contains the following data:

• participant: unique id for each participant

• score_reading: number of points the participant scored in a reading test

• hours_video: average number of hours the participant spends watching video stream (TV, movies, ..) each day.

Tasks

1. Read the data file module1-video_reading.csv into R and assign it to a variable called “dat”.

2. Draw a scatter plot of “hours_video” and “score_reading”.

3. Estimate a linear regression model for “score_reading” as target (dependent variable) and “hours_video” as feature (independent/explanatory variable) using the lm() function.

4. Redo task 3 using the mlr3verse package. Does your final model output (applying the summary() function on the fitted model object) differ from the one in task 3, which was estimated using base R functionality?

5. Add the regression line from task 4 to the plot from task 2.

6. Identify and exclude the outlier, and redo tasks 4 and 5 (i.e., estimate the model again and add the new line to the scatter plot).

7. What would be the reading score (“score_reading”) of a participant with a video consumption (“hours_video”) equivalent to the 95th percentile as predicted by the model? (Hint: you can use the predict_newdata() method on the fitted model object, which behaves similar to the predict() function in base R)

8. Add the prediction from task 7 to the scatter plot from task 6.

9. Bonus: What is the effect of “hours_video” on “score_reading” in standardized units? How would you interpret this effect? (Hint: You can use a linear regression model to determine the correlation between “hours_video” and “score_reading”)