A retailer would like to know if the amount of time a customer spends online has an effect on the amount of money spent by the customer. The file Online_Shopping.xlsx has information on randomly selected customers who made an online purchase. The information contained in this file includes the
amount of time each customer spent viewing items on the website (in minutes)
and the
dollar amount of their purchase. You will use this data to answer Questions 1 and 2
1.
Based on this data, how much of the variation in the dependent variable can be explained by the explanatory variable?
NOTE:
Enter a numerical value only, and round your response to
4
digits after the decimal point. For example, you would enter the value of .34561 as
.3456
in the box below; and .34566 as
.3457. If you do not follow these formatting and rounding rules, you may not receive credit for your answer.
2.
Based on your analysis of data in Question 7, what is the point estimate of the dollar amount of purchase, if the customer stays and browses for
15 minutes? Calculate this point estimate using the actual values obtained in your analysis and not the rounded values used to answer the previous questions.
NOTE:
Enter a numerical value only, and round your response to
2
digits after the decimal point. Do
not
enter the dollar sign ($). For example, you would enter the value of $105.3447 as
105.34
in the box below; and $105.3456 as
105.35. If you do not follow these formatting and rounding rules, you may not receive credit for your answer.
3.
A few years ago I was asked to help a company estimate its shipping costs. The company is in the business of providing authentic rebuilt parts for classic cars. They get orders from all around the world for both small and large parts. The company needed a method to estimate the shipping costs for the ordered parts instead of telling customers "TBD" (To Be Determined), which was becoming less and less acceptable given the wide variation in shipping costs due to the size of the part and the shipping destination.
The file Shipping_Cost.xlsx contains a random sampling of some recent orders (and includes only orders for the 48 contiguous states). The data included are the
total dollar amount of the merchandise to be shipped (Merchandise Total),
the weight of the order (Shipping Weight), and the
actual cost of shipping incurred (Shipping Charge).
Assume that you can appropriately use simple linear regression for this case. Using the data, run the regression models identified below and submit a single PDF file which includes the following components:
a) Model A: Run the regression model with only the
Shipping Weight
as the explanatory variable and the
Shipping Charge
as the dependent variable. Include the Excel output of your model (all three tables in the Summary Output of regression analysis) and label it as Model A.
b) Model B: Run the regression model with only the
Merchandise Total
as the explanatory variable and the
Shipping Charge
as the dependent variable. Include the Excel output of your model (all three tables in the Summary Output of regression analysis) and label it as Model B.
c) Analyze and compare the two models. Is one model better than the other? Why? Explain your answer fully by analyzing the standard deviation of the prediction errors and any other relevant model statistics.
Please Convert Excel File to pdf
Your response must be clear and concise; contain all requested information; and it may not exceed 4 pages and 5 MBs in size.