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# Advertising and price as determinants of sales

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Sam Smith, owner and general manager of Campus Stationery Store, is concerned about the sales behavior of a scanner at the store. He understands that there may be many factors, which may help explain sales, but he believes that advertising and price are major determinants of sales. Sam collects the data given below with:
Y = SALES (# of sales)
X2 = PRICE (\$)

33 3 125 10 130
61 6 115
70 10 113
82 13 130
17 9 145
24 6 140
40 5 120
48 5 116
56 7 110
72 11 108

Regression Analysis: sales versus ads, price

The regression equation is:
sales = 157 + 4.33 ads - 1.14 price

Predictor Coef SE Coef T P
Constant 157.50 33.78 4.66 0.002
PRICE -1.1428 0.2677 -4.27 0.004

S = 10.1422 R-Sq = 82.9% R-Sq(adj) = 78.1%

Analysis of Variance:

Source DF SS MS F P
Regression 2 3502.0 1751.0 17.02 0.002
Residual Error 7 720.1 102.9
Total 9 4222.1

Predicted Values for New Observations

New Obs Fit SE Fit 95% CI 95% PI
1 52.20 4.67 (41.16, 63.25) (25.80, 78.61)

Values of Predictors for New Observations

1 10.0 130

0.055

PRICE -0.661 0.008
0.037 0.982

Cell Contents: Pearson correlation
P-Value

a. Analyze the above output to determine the multiple regression equation
b. Find and interpret the multiple index of determination (R-Sq)
c. Perform the t-tests on ???? ? 1 and on ???? ? 2 (Use two tailed test with (??= 0.05). Interpret your results
d. Predict the number of sales given that there were 10 ads and the price was \$130; use both a point estimate and the appropriate interval estimate