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case study is the word doc; and the data is in excel.

Please see two attached files: case study is the word doc; and the data is in excel.
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Babies R'Us Case Study
You have been hired as a strategic consultant by the hugely successful retailer "Babies R'Us".  The company sells many products, although one product in particular, a highly innovative car seat, is being test marketed in various domestic and international markets.  "Babies R'Us" has hired you to help them better understand the test market data they have compiled from 400 retailers worldwide (found in the excel file called "CarSeats.xls").  The variables in the data set include:
Unit Sales = the number of units sold (in thousands),
Competitor's Price = the price for a similar product being sold by a competitor (in dollars),
Income Level = average household income in the region (in thousands of dollars),
Advertising = amount spent on advertising the product (in thousands of dollars),
Price = the price being charged by your company (in dollars),
Population = number of people (in thousands) living in the region,
Average Age = average age of the population in the region (in years),
Average Education = average educational level in region,
Shelving location = quality of the shelf location for your product (good, medium, or bad), Urban or Rural = description of the region as urban or rural, and finally,
US = a categorical variable indicating whether the sales region is in the US or an international market.

Babies R’Us Case Study
You have been hired as a strategic consultant by the hugely successful
retailer “Babies R’Us”. The company sells many products, although
one product in particular, a highly innovative car seat, is being test
marketed in various domestic and international markets. “Babies
R’Us” has hired you to help them better understand the test market
data they have compiled from 400 retailers worldwide (found in the excel
file called “CarSeats.xls”). The variables in the data set include:
Unit Sales = the number of units sold (in thousands), Competitor’s
Price = the price for a similar product being sold by a competitor (in
dollars), Income Level = average household income in the region (in
thousands of dollars), Advertising = amount spent on advertising the
product (in thousands of dollars), Price = the price being charged by
your company (in dollars), Population = number of people (in thousands)
living in the region, Average Age = average age of the population in the
region (in years), Average Education = average educational level in
region, Shelving location = quality of the shelf location for your
product (good, medium, or bad), Urban or Rural = description of the
region as urban or rural, and finally, US = a categorical variable
indicating whether the sales region is in the US or an international
market.
Run a multiple regression model with Unit Sales as the dependent
variable against all of the available predictor variables, and use that
model to answer the questions below. NOTE: DON’T TRY TO ADD OR
DEPETE ANY OTHER VARIABLES JUST YET, SIMPLY USE ALL VARIABLES AVAILABLE
IN THE ORIGINAL DATA SET:
Which independent variables appear to be important predictors of sales?
Why?
Which are not? Any surprises? Explain!
Do you think collinearity is a problem for this data set? Explain!
Are there outliers? Which one(s)?
If this was your final model, what sort of “regions” should be
targeted in the future? Explain!
If it costs $80 to produce each car seat, and advertising dollars spent
represents the only additional variable cost associated with producing
this product, what would be the effect of increasing the price you
charge for each seat at retail, by $10? Explain!
Some of the international managers feel that the product is not viewed
favorably outside the US, what should you tell those managers? Explain!
Babies R’Us can usually negotiate free shelving space (due to their
volumes) if they can accept a “bad” location. Should they do this?
Explain!
Is there evidence that advertising dollars are being well spent for this
product? Explain!
Now, determine, your own “BEST POSSIBLE MODEL” for predicting sales
(NOTE: This final model might involve new terms that you create based
upon a careful reflection of the possible need for higher order terms,
interaction terms, indicator variables, etc…). Once you have found
what you consider to be the “BEST” model, go back and modify your
answers to the above questions, IF NECESSARY!

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