# GOLDEN GATE UNIVERSITY PMBA 305 Spring'13 Module #4

Question # 00032573 Posted By: spqr Updated on: 11/20/2014 01:44 PM Due on: 12/12/2014
Subject Mathematics Topic General Mathematics Tutorials:
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PMBA 305 - Spring '13

Quantitative Aspects of Decision Making

Module #4: Two-Sample Tests and Simple Linear Regression

Draft Due: March 8, 2013
 Name ID#

## GOLDEN GATE UNIVERSITY

[1] In an article inMarketing Science, Silk and Berndt investigate the output of advertising agencies. They describe ad agency output by finding the shares of dollar billing volume coming from various media categories such as network television, spot television, newspaper, radio, and so forth.

Suppose that a random sample of 400 U.S. advertising agencies gives an average percentage share of billing volume from network television equal to 7.46 percent with a standard deviation of 1.42 percent. Further, suppose that a random sample of 400 U.S. advertising agencies gives an average percentage share of billing volume from spot television commercials equal to 12.44 percent with a standard deviation of 1.55 percent.

Using the sample information, does it appear that the mean percentage share of billing volume from spot television commercials for the U.S. advertising agencies is greater than the mean percentage share of billing volume from network television? Explain.

[2] A random sample of the birth weights of 186 babies has a mean of 3103g and a standard deviation of 696g (based on data from “Cognitive Outcomes of Preschool Children with Prenatal Cocaine Exposure,” by Singer et al.,Journal of the American Medical Association, Vol. 291, No. 20). These babies were born to mothers who did not use cocaine during their pregnancies. Further, a random sample of the birth weights of 190 babies born to mothers who used cocaine during their pregnancies has a mean of 2700g and a standard deviation of 645g. Does cocaine use appear to affect the birth weight of a baby? Substantiate you conclusion.

[3] The owner of an intra -city moving company typically has his most experienced manager predict the total number of labor hours that will be required to complete an upcoming move. This approach had proved useful in the past, but he would like to be able to develop a more accurate method of predicting the labor hours by using the amount of cubic feet moved. In a preliminary effort to provide a more accurate method, he has collected data for 36 moves, in which the travel time was an insignificant portion of the labor hours worked.

The data are in the Excel file, MOVING.xls downloadable from File or click Companion Website atwww.peasronhighered.com/levine, and go to the Excel Date Files link.

a)Set up a scatter diagram.

b)Assuming a linear relationship, find the regression coefficients, b0, b1, and its regression equation.

c)Interpret the meaning of the slope b1 in this problem.

d)Predict the labor hours for moving 500 cubic feet.

e)What factors besides the cubic feet moved might affect labor hours?

f)Determine the coefficient of determination,r2,and interpret its meaning.

g)Find the standard error of the estimate.

h)How useful do you think this regression model is for labor hours?

i)Determine if the assumption of normality is violated by using the normal probability plot for residuals.

j)At the 0.05 level of significance, is there evidence of a linear relationship between the numbers of cubic feet moved and labor hours?

k)Set up a 95% confidence interval estimate of the population slope,b1.

l)Set up a 95% confidence interval estimate of the average labor hours for all moves of 500 cubic feet.

m)Set up a 95% confidence interval of the labor hours of an individual move that has 500 cubic feet.

n)Explain the difference in the results obtained in (l) and (m).

[4] An auto manufacturing company wanted to investigate how the price of one of its car models depreciates with age. The research department at the company took a sample of eight cars of this model and collected the following information on the ages (in years) and prices (in hundreds of dollars) of these cars. The data are in USEDCAR.xls.

 Age (x) 8 3 6 9 2 5 6 3 Price (y) 16 74 40 19 124 36 33 89

a)Find the value of the linear correlation coefficient r.

b)Find the value of the coefficient of determination r2, and interpret the meaning for this problem.

c)At the 0.05 level of significance, is there a significant linear relationship between two variables?

d)Determine the adequacy of the fit of the model.

e)Evaluate whether the assumptions of regression (LINE) have been seriously violated.

f)If there is a linear correlation, what is the regression equation?

g)Interpret the meaning of the slope b1 in this problem.

h)Interpret the meaning of the Y-intercept b0 in this problem. Will it make sense to you as far as this model is concerned? Explain why.

i)Set up a 95% confidence interval estimate of the population slope.

j)Set up a 95% confidence interval estimate of the average price for all cars of this model after 7 years.

k)Set up a 95% confidence interval of the average price of a car of this model after 7 years.

l)Explain the difference in the results obtained in (j) and (k).

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