Beer sales at the Shapiro One-Stop Store are analyzed using temperature and number of people (age 21 or over) on the street as independent variables.
A random sample of 20 days is selected, and the following variables are measured:
Y = the number of six-packs of beer sold each day
X1 = the daily high temperature
X2 = the daily traffic count
The data are analyzed using multiple regression. The partial computer output is in Table P 10.

a. Analyze the correlation matrix.
b. Test the hypothesis H0: βj =0, j = 1, 2, at the .01 significance level.
c. Forecast the volume of beer sold if the high temperature is 60 degrees and the traffic count is 500 people.
d. Calculate R2, and interpret its meaning in terms of this problem.
e. Calculate the standard error of the estimate.
f. Explain how beer sales are affected by an increase of one degree in the high temperature.
g. State your conclusions for this analysis concerning the accuracy of the forecasting equation and also the contributions of the independent variables.

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TABLE P-10 Minitab Output Correlations x1 X1 0.827 X2 Regression Analysis The regression equation is Y26.7 +782 x1 068 2 0.822 0.680 Coef -26.706 -78207 06795 SE Coef Predictor Constant x1 x2 .22694 .02026 R-Sq R-Sq(adj) Analysis of Variance Source Regression Residual Error Total sS 11589.035 2727.914 14316.949 MS 5794.516 160.466 DF 36.11 17 19