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A sample of General Mills employees was studied to determine their degree of satisfaction with their present life. A special index, called the index of satisfaction, was used to measure satisfaction. Six factors were studied: age at the time of first marriage (X1) , annual income (X2) , number of children living (X3) , value of all assets (X4) , status of health in the form of an index (X5) , and the average number of social activities per week (X6) . Suppose the multiple regression equation is: Y' = 16.24 + 0.017X1 + 0.00028X2 + 42X3 + 0.0012X4 + 0.19X5 + 26.8X6. What is the estimated index of satisfaction for a person who first married at 25, has an annual income of $100,000, has two children, has assets of $500,000, has in index of health status of 141, and has 2 social activities per week?


A) 777.7
B) 796.6
C) 588.6
D) 601.6
E) 809.1

F) B) and C)
G) A) and B)

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What does the correlation matrix for a multiple regression analysis contain?


A) Multiple correlation coefficients
B) Simple correlation coefficients
C) Multiple coefficients of determination
D) Multiple standard errors of estimate

E) None of the above
F) B) and C)

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If the coefficient of multiple determinations is 0.81, what percent of variation is not explained?


A) 19%
B) 90%
C) 66%
D) 81%

E) A) and B)
F) All of the above

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i. The values ofb1, b2 and b3in a multiple regression equation are called the net regression coefficients. They indicate the change in the predicted value for a unit change in one X when the other X variables are held constant. ii. Multiple regression analysis examines the relationship of several dependent variables on the independent variable. iii. A multiple regression equation defines the relationship between the dependent variable and the independent variables in the form of an equation.


A) (i) , (ii) , and (iii) are all correct statements.
B) (i) and (ii) are correct statements but not (iii) .
C) (i) and (iii) are correct statements but not (ii) .
D) (ii) and (iii) are correct statements but not (i) .
E) (i) , (ii) , and (iii) are all false statements.

F) All of the above
G) C) and D)

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The following correlations were computed as part of a multiple regression analysis that used education, job, and age to predict income. The following correlations were computed as part of a multiple regression analysis that used education, job, and age to predict income. Which is the dependent variable? A) Income B) Age C) Education D) Job Which is the dependent variable?


A) Income
B) Age
C) Education
D) Job

E) All of the above
F) A) and B)

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The coefficient of determination measures the proportion of


A) explained variation relative to total variation.
B) variation due to the relationship among variables.
C) error variation relative to total variation.
D) variation due to regression.

E) A) and D)
F) B) and C)

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A real estate agent developed a model to relate a house's selling price (Y) to the area of floor space (X) and the area of floor space squared (X2) . The multiple regression equation for this model is: Y = 125 - 3X + X2 where: Y = selling price (times $1,000) X = square feet of floor space (times 100) What is the difference in selling prices of a house with 1,600 square feet and one with 1,700 square feet?


A) $30 000
B) $3000
C) $363 000
D) $333 000

E) A) and B)
F) B) and D)

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When does multicollinearity occur in a multiple regression analysis?


A) Dependent variables are highly correlated.
B) Independent variables are minimally correlated.
C) Independent variables are highly correlated.
D) Independent variables have no correlation.

E) B) and C)
F) A) and C)

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A sample of General Mills employees was studied to determine their degree of satisfaction with their present life. A special index, called the index of satisfaction, was used to measure satisfaction. Six factors were studied: age at the time of first marriage (X1) , annual income (X2) , number of children living (X3) , value of all assets (X4) , status of health in the form of an index (X5) , and the average number of social activities per week (X6) . Suppose the multiple regression equation is: Y' = 16.24 + 0.017X1 + 0.00028X2 + 42X3 + 0.0012X4 + 0.09X5 + 26.8X6. Explain the meaning of b2.


A) For each additional$1,000 of annual income, their satisfaction index is expected to increase by 0.28 points.
B) For each additional$1,000 of annual income, their satisfaction index is expected to increase by 2.8 points.
C) For each additional$1,000 of annual income, their satisfaction index is expected to increase by 28 points.
D) For each additional living child, their satisfaction index is expected to increase by 42 points.
E) For each additional living child, their satisfaction index is expected to increase by 4.2 points.

F) A) and D)
G) A) and C)

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The production of automobile tires in any given year is related to the number of automobiles produced this year and in prior years. Suppose our econometric model resulted in the following data. The production of automobile tires in any given year is related to the number of automobiles produced this year and in prior years. Suppose our econometric model resulted in the following data. Which variable in the model is the most significant predictor of tire production? A) X<sub>1</sub> B) X<sub>2</sub> C) X<sub>3</sub> D) X<sub>4</sub> E) X<sub>5</sub> Which variable in the model is the most significant predictor of tire production?


A) X1
B) X2
C) X3
D) X4
E) X5

F) B) and E)
G) C) and D)

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Angela Chou has been asked to investigate the determinants of poverty in Ontario communities. She collected data on 60 communities from Statistics Canada. She selected the percentage of poor persons living under the poverty line [Poor (%) ], measured by Low Income Cut-Off, designed by Statistics Canada as a measure of poverty for a community, as the dependent variable. The independent variables selected are percent of single families in each community, the unemployment rate in each community, percent of population in the community holding a bachelor's degree as their highest level of education attained, and percent of population holding a High School Diploma as their highest level of education attained. Given the regression equation Poor (%) = -3.81 + 0.798 Single-Families (%) + 0.624 Unemployment Rate (%) - 0.170 Bachelor's Degree (%) - 0.003 High School (%) . How many dependent variables are there in this regression?


A) 1
B) 2
C) 3
D) 4
E) 5

F) A) and B)
G) C) and E)

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In multiple regression, a dummy variable can be included in a multiple regression model as


A) an additional quantitative variable.
B) a nominal variable with three or more values.
C) a nominal variable with only two values.
D) a new regression coefficient.

E) A) and C)
F) None of the above

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The production of automobile tires in any given year is related to the number of automobiles produced this year and in prior years. Suppose our econometric model resulted in the following data. The production of automobile tires in any given year is related to the number of automobiles produced this year and in prior years. Suppose our econometric model resulted in the following data. How much does tire production increase for every thousand cars produced two years ago? A) 670 B) 67 C) 0.67 D) 6.7 How much does tire production increase for every thousand cars produced two years ago?


A) 670
B) 67
C) 0.67
D) 6.7

E) A) and B)
F) A) and C)

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A sample of General Mills employees was studied to determine their degree of satisfaction with their present life. A special index, called the index of satisfaction, was used to measure satisfaction. Six factors were studied: age at the time of first marriage (X1) , annual income (X2) , number of children living (X3) , value of all assets (X4) , status of health in the form of an index (X5) , and the average number of social activities per week (X6) . Suppose the multiple regression equation is: Y' = 16.24 + 0.017X1 + 0.00028X2 +42X3 + 0.0012X4 + 0.09X5 + 26.8X6. Explain the meaning of b5.


A) For each additional10 points on the health status index, their satisfaction index is expected to increase by 9 points.
B) For each additional10 points on the health status index, their satisfaction index is expected to increase by 0.09 points.
C) For each additional10 points on the health status index, their satisfaction index is expected to increase by 0.9 points.
D) For each additional10 points on the health status index, their satisfaction index is expected to drop by 0.09 points.
E) For each additional10 points on the health status index, their satisfaction index is expected to decrease by 9 points.

F) A) and E)
G) A) and C)

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The following correlations were computed as part of a multiple regression analysis that used education, job, and age to predict income. The following correlations were computed as part of a multiple regression analysis that used education, job, and age to predict income. Which independent variable has the weakest association with the dependent variable? A) Income B) Age C) Education D) Job Which independent variable has the weakest association with the dependent variable?


A) Income
B) Age
C) Education
D) Job

E) A) and D)
F) A) and B)

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A manager at a local bank analyzed the relationship between monthly salary and three independent variables: length of service (measured in months) , gender (0 = female, 1 = male) and job type (0 = clerical, 1 = technical) . The following ANOVA summarizes the regression results: A manager at a local bank analyzed the relationship between monthly salary and three independent variables: length of service (measured in months) , gender (0 = female, 1 = male) and job type (0 = clerical, 1 = technical) . The following ANOVA summarizes the regression results: The results for the variable gender show that, A) males average $222.78 more than females in monthly salary. B) females average $222.78 more than males in monthly salary. C) gender is not related to monthly salary. D) gender and months of service are correlated. The results for the variable gender show that,


A) males average $222.78 more than females in monthly salary.
B) females average $222.78 more than males in monthly salary.
C) gender is not related to monthly salary.
D) gender and months of service are correlated.

E) A) and B)
F) B) and D)

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How is the degree of association between the set of independent variables and the dependent variable is measured?


A) Confidence intervals
B) Autocorrelation
C) Coefficient of multiple determination
D) Standard error of estimate

E) None of the above
F) B) and D)

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Twenty-one executives in a large corporation were randomly selected for a study in which several factors were examined to determine their effect on annual salary (expressed in $000's) . The factors selected were age, seniority, years of college, number of company divisions they had been exposed to and the level of their responsibility. A regression analysis was performed using a popular spreadsheet program with the following regression output: Twenty-one executives in a large corporation were randomly selected for a study in which several factors were examined to determine their effect on annual salary (expressed in $000's) . The factors selected were age, seniority, years of college, number of company divisions they had been exposed to and the level of their responsibility. A regression analysis was performed using a popular spreadsheet program with the following regression output: What is the effect on salary for an increase of one level of responsibility if the other variables are held constant? A) +$3,554 B) -$3,554 C) +$833 D) +$8330 What is the effect on salary for an increase of one level of responsibility if the other variables are held constant?


A) +$3,554
B) -$3,554
C) +$833
D) +$8330

E) All of the above
F) A) and C)

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A manager at a local bank analyzed the relationship between monthly salary and three independent variables: length of service (measured in months) , gender (0 = female, 1 = male) and job type (0 = clerical, 1 = technical) . The following ANOVA summarizes the regression results: A manager at a local bank analyzed the relationship between monthly salary and three independent variables: length of service (measured in months) , gender (0 = female, 1 = male) and job type (0 = clerical, 1 = technical) . The following ANOVA summarizes the regression results: Based on the hypothesis tests for the individual regression coefficients, A) all the regression coefficients are not equal to zero. B) job is the only significant variable in the model. C) only months of service and gender are significantly related to monthly salary. D) service is the only significant variable in the model. Based on the hypothesis tests for the individual regression coefficients,


A) all the regression coefficients are not equal to zero.
B) "job" is the only significant variable in the model.
C) only months of service and gender are significantly related to monthly salary.
D) "service" is the only significant variable in the model.

E) A) and D)
F) A) and B)

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It has been hypothesized that overall academic success for freshmen at college as measured by grade point average (GPA) is a function of IQ scores (X1) , hours spent studying each week (X2) , and one's high school average (X3) . Suppose the regression equation is: Y' = -6.9 + 0.055X1 + 0.107X2 + 0.0083X3. The multiple standard error is 6.313 and R2= 0.826. What will the GPA be if the number of hours spent studying is 30 the IQ is 108, and the high school average is 82?


A) 3.1446
B) 2.9306
C) 0.428
D) 10.0446
E) 13.1892

F) A) and E)
G) C) and D)

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