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More lags

It might be interesting to learn if coupon actions only cause an immediate effect on sales or if the effect continues into the next period.

Again, you shift the COUPON predictor back by using the function lag(). The result is assigned to a new variable Coupon.lag. Both, the original COUPON and the Coupon.lag variables, are added to the log(SALES) ~ PRICE + Price.lag relationship by using the update() function.

Este ejercicio forma parte del curso

Building Response Models in R

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Instrucciones del ejercicio

  • Create a lagged variable for COUPON named Coupon.lag.
  • Update the lag.model object for COUPON and Coupon.lag by using the function update().

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# Take the lag of COUPON
Coupon.lag <- ___

# Update the model for COUPON and Coupon.lag
___(lag.model, . ~ . + ___ + ___)
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