# Project #18092 - case statistics

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

Instructions:

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.

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

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

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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