Chapter 14

Front 1

In a multiple regression problem involving two independent variables, if b1 is computed to be +2.0, it means that

Back 1

the estimated mean of Y increases by 2 units for each increase of 1 unit of X1, holding X2 constant.

Front 2

TABLE 14-2

A professor of industrial relations believes that an individual's wage rate at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects the following information:

REF QUIZ14 #2 TABLE 14-2


Referring to Table 14-2, for these data, what is the value for the regression constant, b0?

Back 2

6.932

Front 3

TABLE 14-2

A professor of industrial relations believes that an individual's wage rate at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects the following information:

REF QUIZ14 #3 TABLE 14-2


Referring to Table 14-2, for these data, what is the estimated coefficient for performance rating, b1?

Back 3

1.054

Front 4

TABLE 14-2

A professor of industrial relations believes that an individual's wage rate at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects the following information:

REF QUIZ14 #4 TABLE 14-2


Referring to Table 14-2, for these data, what is the estimated coefficient for the number of economics courses taken, b2?

Back 4

0.616

Front 5

TABLE 14-2

A professor of industrial relations believes that an individual's wage rate at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects the following information:


REF QUIZ14 #5 TABLE 14-2

Referring to Table 14-2, suppose an employee had never taken an economics course and managed to score a 5 on his performance rating. What is his estimated expected wage rate?

Back 5

12.20

Front 6

TABLE 14-2

A professor of industrial relations believes that an individual's wage rate at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects the following information:


REF QUIZ14 #6 TABLE 14-2

Referring to Table 14-2, an employee who took 12 economics courses scores 10 on the performance rating. What is her estimated expected wage rate?

Back 6

24.87

Front 7

TABLE 14-3

An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below.

SUMMARY OUTPUT


REF QUIZ14 #7 TABLE 14-3

Referring to Table 14-3, to test for the significance of the coefficient on aggregate price index, the p-value is

Back 7

0.8330.

Front 8

TABLE 14-3

An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below.

SUMMARY OUTPUT


REF QUIZ14 #8 TABLE 14-3

Referring to Table 14-3, to test for the significance of the coefficient on gross domestic product, the p-value is

Back 8

0.0001.

Front 9

TABLE 14-4

A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel output is provided below:


REF QUIZ14 #9 TABLE 14-4

Referring to Table 14-4, what fraction of the variability in house size is explained by income, size of family, and education?

Back 9

74.8%

Front 10

TABLE 14-4

A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel output is provided below:


REF QUIZ14 #10 TABLE 14-4

Referring to Table 14-4, which of the independent variables in the model are significant at the 5% level?

Back 10

Income, Size

Front 11

TABLE 14-4

A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel output is provided below:


REF QUIZ14 #11 TABLE 14-4

Referring to Table 14-4, at the 0.01 level of significance, what conclusion should the builder reach regarding the inclusion of Income in the regression model?

Back 11

Income is significant in explaining house size and should be included in the model because its p-value is less than 0.01.

Front 12

TABLE 14-6

One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) the amount of insulation in inches (X2), the number of windows in the house (X3), and the age of the furnace in years (X4). Given below are the Excel outputs of two regression models.

Model 1


Model 2

REF QUIZ14 #12 TABLE 14-6

Referring to Table 14-6, the estimated value of the partial regression parameter 1 in Model 1 means that

Back 12

holding the effect of the other independent variables constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs by $4.51.

Front 13

TABLE 14-6

One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) the amount of insulation in inches (X2), the number of windows in the house (X3), and the age of the furnace in years (X4). Given below are the Excel outputs of two regression models.

Model 1


Model 2

REF QUIZ14 #13 TABLE 14-6

Referring to Table 14-6, what is your decision and conclusion for the test H0: 2 = 0 vs H1: 2 < 0 at the = 0.01 level of significance using Model 1?
.

Back 13

Reject H0 and conclude that the amount of insulation has a negative linear effect on heating costs

Front 14

A dummy variable is used as an independent variable in a regression model when

Back 14

the variable involved is categorical

Front 15

To explain personal consumption (CONS) measured in dollars, data is collected for

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A regression analysis was performed with CONS as the dependent variable and ln(CRDTLIM), ln(APR), ln(ADVT), and GENDER as the independent variables. The estimated model was

Y = 2.28 - 0.29 1n(CRDTLIM) + 5.77 1n(APR) + 2.35 In(ADVT) + 0.39 GENDER, with 0 being used as an index for males and 1 as an index for females.

What is the correct interpretation for the estimated coefficient for GENDER?

Back 15

Holding the effect of the other independent variables constant, mean personal consumption for females is estimated to be $0.39 higher than males.