Suppose you are consulting for a cereal brand that ran a media campaign over 8-w

Question

Suppose you are consulting for a cereal brand that ran a media campaign over 8-weeks back in 2016. You have collected their sales data and ran the following model: WeeklySales = Intercept + beta_1 TVSpend + beta_2 PressSpend + u where WeeklySales are store-level weekly sales of this cereal product, $ TVSpend is TV ad spend per week during this ad campaign, $ PressSpend is Press ad spend per week during this ad campaign, $. Suppose after you run your model you get the following output Interpret coefficients on Intercept, TVSpend and PressSpend. Do they make sense? Derive 95% confidence intervals for these variables, assuming large sample size. What is the economic meaning of these confidence intervals? How should we interpret them back to the cereal brand management? What could have caused a negative PressSpend variable? How might it affect other coefficients in the model? Explain.

Explanation / Answer

a)

Intercept is expected mean value of weeklysales when TVspend and PRESSspend is 0. Here it is 1000.43 when TVspend and PRESSspend is 0.

TVSpend : A unit increase in TVSpend will increase weeklysales by 4.84090 keeping all other variables constant.

PRESSspend : Aunit increase in PRESSspend will in decrease weeklysales by -1.34455 keeping all other variables contant.

b) 95% Confidenc interval

   TVSpend upper conf interval= mean of TVSpend + 1.96* 0.001600

             Lower conf interval = mean of TVSpend - 1.96* 0.001600

PRESSspend upper conf interval= mean of PRESSspend + 1.96* 0.768899
             Lower conf interval = mean of PRESSspend - 1.96* 0.768899

c)

Weekly sales was negatively impacted by PRESSspend hence we have negative coefficient.

It will always imapct the weekly sales when other variables are positive.

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