Project #18092 - case statistics

In this case you will apply statistical techniques learned in the Regression part of BUAD 310. 

 

Instructions: 

 

• This assignment uses data from the file MagAds.xlsx, which you can download from 

Blackboard. After you download the file go to Data → Load data → from file in 

StatCrunch to open it (you don’t need to change any of the options when loading 

this data.) 

 

• The entire report should be typed and clearly presented without typos and 

grammatical errors. Copy and paste the relevant (explained further in more detail) 

regression output into your document. Do not attach any graphs. 

 

• You are encouraged to work in groups (maximum size is 5) and submit one 

assignment for the whole group. A hard copy of the assignment needs to be 

submitted (rather than an electronic copy) to avoid a penalty on the grade. 

 

• Very important: present the problems in exactly the same order as they are listed. 

 

 

Magazine Advertising 

 

What factors influence the price of advertisements in magazines? Suppose you are part of a team 

of consultants hired by a retail clothing company wishing to place advertisements in at least one 

magazine. They are curious about what types of costs they can expect for magazines with 

different readership bases so they most effectively utilize their advertising budget. Your team has 

collected cost data on 44 consumer magazines. In addition, your team has measured some other 

characteristics of the magazines and their audiences that may be useful in understanding the 

advertisement costs better. The variables are as follows, 

 

 pagecost: Cost of a four-color, one-page ad (in dollars) 

 circ: Circulation (projected, in thousands) 

 percmale: Percent male among the predicted readership 

 medianincome: Median household income of readership (in dollars) 

 

Some natural logarithms of the variables are also provided for your convenience. Your goal is to 

analyze the data with StatCrunch using Multiple Linear Regression methods and choose the best 

model to explain the differences in advertising costs between the different titles and to predict 

what the retail clothing company should expect to pay for advertising in the different magazines. 

 

  

Answer the following questions (with reasonable detail, not just “yes” or “no”, use one or 

two sentences per question). 

 

 

1. Perform a Regression analysis to predict pagecost using all three explanatory variables 

 

[Stat → Regression → Multiple Linear, then fill in the proper Y variable and X variables 

(hold the “Ctrl” key when selecting the X variables), then scroll to the bottom of the screen 

and under Save options select (holding Ctrl) Residuals, Predicted values and 95% confidence 

interval for individual prediction. For the prediction interval to be produced you need to first 

enter the values from part d in the row underneath the data table, in appropriate columns. 

Note that the value for circ has to be entered in the same units as all the values in the circ 

column. To produce a residual plot, go to [Graphics→ Scatter Plot], then select Residuals as 

the Y variable and Predicted values as the X variable]. 

 

 

Include the regression output (only the coefficient and ANOVA tables), but not the plot. 

 

 

a. Explain, in simple terms, the R-squared value that you got. 

 

 

b. Evaluate the regression assumptions by assessing the residual plot. 

 

c. Examine each of the explanatory variables individually to determine which are 

contributing significantly to the model. (Use the significance level of 5 %. Do NOT 

actually eliminate any variables from the regression at this stage.) 

 

d. Provide an appropriate 95%-level interval to the retail clothing company for the amount 

that they would pay for a full-page ad in a magazine with a projected audience of 

1,400,000 readers, 50 percent of which are male, with a median income of $22,000. 

 

Explain in one sentence and in simple terms what this interval means.

Subject Business
Due By (Pacific Time) 11/30/2013 12:00 am
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