Understanding dummy variables
Selling craft beer is highly competitive. Increasing in-store visibility usually generates additional sales. Therefore, the brewery makes use of point-of-sales display ads. The volume sales of Hoppiness were recorded for all weeks with and without displays.
It is useful to start with examining log(SALES) separately for DISPLAY and no-DISPLAY activities. You can do this by using the function aggregate(). The aggregate() function can also operate on formula statements, a feature making its usage quite handy. Here, log(SALES) ~ DISPLAY groups log(SALES) according to the levels in DISPLAY. The function argument FUN applies a specified function to each level. Again, you calculate some simple descriptive measures using the functions mean(), min() and max().
This exercise is part of the course
Building Response Models in R
Exercise instructions
- Calculate the mean of
log(SALES)for each level inDISPLAY. - Calculate the minimum of
log(SALES)for each level inDISPLAY. - Calculate the maximum of
log(SALES)for each level inDISPLAY.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Mean log(SALES)
aggregate(___ ~ ___, FUN = ___, data = sales.data)
# Minimum log(SALES)
aggregate(___, FUN = ___, data = sales.data)
# Maximum log(SALES)
___(___, FUN = ___, data = sales.data)