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().
Deze oefening maakt deel uit van de cursus
Building Response Models in R
Oefeninstructies
- 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.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Mean log(SALES)
aggregate(___ ~ ___, FUN = ___, data = sales.data)
# Minimum log(SALES)
aggregate(___, FUN = ___, data = sales.data)
# Maximum log(SALES)
___(___, FUN = ___, data = sales.data)