document with your screenshots

You must submit 1 word document with your screenshots and answers. You should also submit the file where you conducted your statistical analysis (Excel) ______________________________________________________________________________…

You must submit 1 word document with your screenshots and answers.
You should also submit the file where you conducted your statistical
analysis (Excel)
______________________________________________________________________________
Consider yourself working for a global retailer that over the years
has added a web-based channel to their physical store locations. Now,
after learning more about mobile-led changes in retailing, they are
excited about what the mobile ecosystem offers. They are seeking your
help as they embark on using mobile as a channel. They want to
commission an app development team to deploy a presence on iOS and
Android. However, several questions arise about the deployment of the
app. Your job is to provide data driven insights to help them
navigate this complex landscape. Specifically, you are tasked with:

1. Using the
data, estimate a linear model for the relationship between demand and
price. For this you have access to a large volume of app level data
(in a file called assignment_2_apps.csv), including information about
the ‘rank’ of the app on the app store. Assume rank =
(1/sales)*1,000,000 (don’t worry about the details behind this
assumption, just make the assumption). Specifically, estimate a
univariate regression where the dependent variable is sales and the
independent variable is price. Provide a screen shot of both
estimated coefficients and the associated P-Values. Briefly explain
the interpretation of the coefficient associated with price. Provide
an explanation for what the P-Value (associated with the price
coefficient) indicates (be very specific). (2 Points)

2. Create a
dummy/binary variable for region. This variable should have a value
of 1 if the region is CN (China) and 0 if the region is US (USA).
Estimate a univariate regression of sales on this newly created
variable. Provide a screenshot and an interpretation of both
estimated coefficients. Be specific. (2 Points)

3. Create another
dummy/binary variable for in app advertisements. This variable should
have a value of 1 if the device has in app advertising and a value of
0 if the device does NOT have in app advertising. Estimate a
regression of sales on the dummy variable created in part 2 and this
newly created dummy variable (all in the same model). Provide a
screenshot of the results and provide an interpretation of all the
coefficients. (2 Points)

4. Estimate a
univariate regression of sales on rank (similar to part 1) except in
this case your model should able to speak in terms of elasticity. By
elasticity you want to speak to your management in percentage terms –
what is the % change in sales for a % increase in price? (Tip: we do
this using log-log-regression models.) Since price can have a value
of 0, you will have to adjust the variable. You can do this by adding
1 to each price and then taking the log. Provide a screenshot of the
results and provide an interpretation for the coefficients.
(https://stats.idre.ucla.edu/other/multpkg/faq/general/faqhow-do-i-interpret-a-regression-model-when-some-variablesare-log-transformed/)
(2 Points)

5. The app
retailer believes that other factors, specifically the app store, the
filesize, the number of screenshots, and the average rating may also
be associated with sales. The retailers want a model that estimates
the relationship between price and sales (similar to 4) except they
want the impact of the above mentioned factors (app store, filesize,
etc.) to be controlled for. Estimate a model that accomplishes this.
Your model should speak in terms of elasticity. Provide screenshots
of your results and discuss how this model achieves what the
retailers want. Provide an interpretation of all the estimated
coefficients. (3 Points)

6. The retailer
is also interested in understanding the impact of the in-app purchase
option. Specifically, the retailer believes that the relationship
between price and sales is different for apps with an in-app purchase
option and apps without an in-app purchase option. They are worried
that if they just create two subsamples (with and without the in-app
purchase option) they will not adequately capture the variation in
the other variables beyond price. Therefore, they want to estimate a
model that allows the relationship between price and sales to be
different based upon whether the app has in-app purchasing or not.
The model should speak in elasticity terms. Do NOT control for the
variables that you controlled for in problem 5. Write the model you
will estimate. Estimate this model and present a screenshot of the
results. Provide an interpretation of all the estimated coefficients.
(4 Points)
Let’s block ads! (Why?)

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