Basic Business Statistics, Student Value Edition
Basic Business Statistics, Student Value Edition
14th Edition
ISBN: 9780134685113
Author: Mark L. Berenson, David M. Levine, David F. Stephan, Kathryn Szabat
Publisher: PEARSON
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Chapter 15, Problem 16PS
To determine

Perform multiple regression analysis and determine VIF for each independent variable in the model and whether there is collinearity.

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Although the Excel regression output, shown in Figure 12.21 for Demonstration Problem 12.1, is somewhat different from the Minitab output, the same essential regression features are present. The regression equation is found under Coefficients at the bottom of ANOVA. The slope or coefficient of x is 2.2315 and the y-intercept is 30.9125. The standard error of the estimate for the hospital problem is given as the fourth statistic under Regression Statistics at the top of the output, Standard Error = 15.6491. The r2 value is given as 0.886 on the second line. The t test for the slope is found under t Stat near the bottom of the ANOVA section on the “Number of Beds” (x variable) row, t = 8.83. Adjacent to the t Stat is the p-value, which is the probability of the t statistic occurring by chance if the null hypothesis is true. For this slope, the probability shown is 0.000005. The ANOVA table is in the middle of the output with the F value having the same probability as the t statistic,…
Question 9  Assume a regression analysis yields a regression line with the value Y=$120,000 + $0.58X, where Y equals plant labor costs and X equals dollars of production output.  If the company plans to produce $2,400,000 of product during the upcoming month, it would project plant labor costs to be:  a. $324,000 b. $120,000 c. $204,000 d. $2,400,000
Let Yt be the sales during month t (in thousands of dollars) for a photography studio, and let Pt be the price charged for portraits during month t. The data are in the file Week 4 Assignment Chapter 12 Problem 64. Use regression to fit the following model to these data:Yt = a + b1Yt−1 + b2Pt + etThis equation indicates that last month’s sales and the current month’s price are explanatory variables. The last term, et, is an error term. If the price of a portrait during month 21 is $10, what would you predict for sales in month 21? Sales Price $400,000 $15 $1,042,000 $12 $1,129,000 $24 $1,110,000 $18 $1,336,000 $18 $1,363,000 $30 $1,177,000 $27 $603,000 $24 $582,000 $36 $697,000 $27 $586,000 $24 $673,000 $27 $546,000 $30 $334,000 $33 $27,000 $24 $76,000 $27 $298,000 $30 $746,000 $18 $962,000 $21 $907,000 $24

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Basic Business Statistics, Student Value Edition

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