WEEK 15 ACTIVITY 10
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WEEK 15 ACTIVITY 10
Name: Akul Patel
UC ID: 005011924
Your firm is attempting to learn the effectiveness of a newly developed television ad on its sales.
To do this, it has randomly run the ad between 0 and 5 times during one week across a large
number of television markets in the United States. It then recorded product sales for the
following month for each market. To conduct the analysis, analysts at the firm have assumed the
following data-generating process:
Sales
i
=α+βAds
i
+U
i
Regressing Sales on Ads yields βˆ=350. The firm would like to use this number to project the
change in Sales when increasing weekly television ads to 20.
1.
According to these results, what is the expected change in Sales when Ads increase
from 5 to 20?
Sales When Ads is 5,
S1 = α+[350*5]+U
i
Sales When Ads is 20 S2 = α+[350*20]+U
i
Expected change is = S2-S1 = 7000-1750 = 5250
2.
Why should we be skeptical of our result from Part a?
Extrapolation: The projection from a coefficient estimated within a limited range (0 to
5 ads) to predict the effect of 20 ads assumes a linear relationship holds beyond the
observed data, which may not be accurate.
Omitted Variables: The regression model only considers the number of ads as a
predictor, neglecting other potential influential factors like market conditions,
seasonality, and competitors' actions.
Endogeneity: The relationship between ads and sales could be bidirectional,
potentially leading to reverse causality and affecting the predicted impact of increased
ads on sales.
Assumption of Constant Effect: The model assumes a consistent linear effect of ads
on sales, disregarding potential diminishing returns or nonlinear relationships.
3.
What can you do to find an estimate of the effect of increasing Ads from 5 to 20 that
is more credible?
Collect More Data: Expanding the data range can account for complexities and
nonlinearities in the relationship.
Experimentation: Controlled experiments isolate the true effect by varying ads and
addressing endogeneity and omitted variables.
Include Relevant Variables: Incorporate factors like demographics and competitors to
create a more comprehensive model.
Time-Series Analysis: Analyzing historical data captures temporal patterns and
seasonal effects.
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