## Statistics for Managers

Statistics

## Quiz 15 :

Multiple Regression Model Building

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Q01 Q01 Q01

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 constructed the multiple regression model.The business literature involving human capital shows that education influences an individual's annual income.Combined,these may influence family size.With this in mind,what should the real estate builder be particularly concerned with when analyzing the multiple regression model?

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B

Q02 Q02 Q02

A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.A statistical analyst discovers that capital spending by corporations has a significant inverse relationship with wage spending.What should the microeconomist who developed this multiple regression model be particularly concerned with?

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B

Q08 Q08 Q08

TABLE 15-1
A certain type of rare gem serves as a status symbol for many of its owners.In theory,for low prices,the demand increases and it decreases as the price of the gem increases.However,experts hypothesize that when the gem is valued at very high prices,the demand increases with price due to the status owners believe they gain in obtaining the gem.Thus,the model proposed to best explain the demand for the gem by its price is the quadratic model:
Y = Î²

_{0}+ Î²_{1}X + Î²_{2}X^{2}+ Îµ where Y = demand (in thousands)and X = retail price per carat. This model was fit to data collected for a sample of 12 rare gems of this type.A portion of the computer analysis obtained from Microsoft Excel is shown below: -Referring to Table 15-1,what is the value of the test statistic for testing whether there is an upward curvature in the response curve relating the demand (Y)and the price (X)?Free

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Q09 Q09 Q09

TABLE 15-1
A certain type of rare gem serves as a status symbol for many of its owners.In theory,for low prices,the demand increases and it decreases as the price of the gem increases.However,experts hypothesize that when the gem is valued at very high prices,the demand increases with price due to the status owners believe they gain in obtaining the gem.Thus,the model proposed to best explain the demand for the gem by its price is the quadratic model:
Y = Î²

_{0}+ Î²_{1}X + Î²_{2}X^{2}+ Îµ where Y = demand (in thousands)and X = retail price per carat. This model was fit to data collected for a sample of 12 rare gems of this type.A portion of the computer analysis obtained from Microsoft Excel is shown below: -Referring to Table 15-1,what is the p-value associated with the test statistic for testing whether there is an upward curvature in the response curve relating the demand (Y)and the price (X)?Free

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Q10 Q10 Q10

TABLE 15-1
A certain type of rare gem serves as a status symbol for many of its owners.In theory,for low prices,the demand increases and it decreases as the price of the gem increases.However,experts hypothesize that when the gem is valued at very high prices,the demand increases with price due to the status owners believe they gain in obtaining the gem.Thus,the model proposed to best explain the demand for the gem by its price is the quadratic model:
Y = Î²

_{0}+ Î²_{1}X + Î²_{2}X^{2}+ Îµ where Y = demand (in thousands)and X = retail price per carat. This model was fit to data collected for a sample of 12 rare gems of this type.A portion of the computer analysis obtained from Microsoft Excel is shown below: -Referring to Table 15-1,what is the correct interpretation of the coefficient of multiple determination?Free

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Q11 Q11 Q11

TABLE 15-1
A certain type of rare gem serves as a status symbol for many of its owners.In theory,for low prices,the demand increases and it decreases as the price of the gem increases.However,experts hypothesize that when the gem is valued at very high prices,the demand increases with price due to the status owners believe they gain in obtaining the gem.Thus,the model proposed to best explain the demand for the gem by its price is the quadratic model:
Y = Î²

_{0}+ Î²_{1}X + Î²_{2}X^{2}+ Îµ where Y = demand (in thousands)and X = retail price per carat. This model was fit to data collected for a sample of 12 rare gems of this type.A portion of the computer analysis obtained from Microsoft Excel is shown below: -Referring to Table 15-1,does there appear to be significant upward curvature in the response curve relating the demand (Y)and the price (X)at 10% level of significance?Free

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Q12 Q12 Q12

TABLE 15-1
A certain type of rare gem serves as a status symbol for many of its owners.In theory,for low prices,the demand increases and it decreases as the price of the gem increases.However,experts hypothesize that when the gem is valued at very high prices,the demand increases with price due to the status owners believe they gain in obtaining the gem.Thus,the model proposed to best explain the demand for the gem by its price is the quadratic model:
Y = Î²

_{0}+ Î²_{1}X + Î²_{2}X^{2}+ Îµ where Y = demand (in thousands)and X = retail price per carat. This model was fit to data collected for a sample of 12 rare gems of this type.A portion of the computer analysis obtained from Microsoft Excel is shown below: -True or False: Referring to Table 15-1,a more parsimonious simple linear model is likely to be statistically superior to the fitted curvilinear for predicting sale price (Y).Free

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True False

Q13 Q13 Q13

TABLE 15-2
In Hawaii,condemnation proceedings are under way to enable private citizens to own the property that their homes are built on.Until recently,only estates were permitted to own land,and homeowners leased the land from the estate.In order to comply with the new law,a large Hawaiian estate wants to use regression analysis to estimate the fair market value of the land.The following model was fit to data collected for n = 20 properties,10 of which are located near a cove.
Model 1: Y = + Î²

_{0}+ Î²_{1}X_{1}+ Î²_{2}X_{2}+ Î²_{3}X_{1}X_{2 }+ Î²_{4}_{ }+ Î²_{S}_{ }X_{2}+ Îµ where Y = Sale price of property in thousands of dollars X_{1}= Size of property in thousands of square feet X_{2}= 1 if property located near cove,0 if not Using the data collected for the 20 properties,the following partial output obtained from Microsoft Excel is shown: -Referring to Table 15-2,is the overall model statistically adequate at a 0.05 level of significance for predicting sale price (Y)?Free

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Q14 Q14 Q14

TABLE 15-2
In Hawaii,condemnation proceedings are under way to enable private citizens to own the property that their homes are built on.Until recently,only estates were permitted to own land,and homeowners leased the land from the estate.In order to comply with the new law,a large Hawaiian estate wants to use regression analysis to estimate the fair market value of the land.The following model was fit to data collected for n = 20 properties,10 of which are located near a cove.
Model 1: Y = + Î²

_{0}+ Î²_{1}X_{1}+ Î²_{2}X_{2}+ Î²_{3}X_{1}X_{2 }+ Î²_{4}_{ }+ Î²_{S}_{ }X_{2}+ Îµ where Y = Sale price of property in thousands of dollars X_{1}= Size of property in thousands of square feet X_{2}= 1 if property located near cove,0 if not Using the data collected for the 20 properties,the following partial output obtained from Microsoft Excel is shown: -Referring to Table 15-2,given a quadratic relationship between sale price (Y)and property size (X_{1}),what null hypothesis would you test to determine whether the curves differ from cove and non-cove properties?Free

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Q15 Q15 Q15

TABLE 15-2
In Hawaii,condemnation proceedings are under way to enable private citizens to own the property that their homes are built on.Until recently,only estates were permitted to own land,and homeowners leased the land from the estate.In order to comply with the new law,a large Hawaiian estate wants to use regression analysis to estimate the fair market value of the land.The following model was fit to data collected for n = 20 properties,10 of which are located near a cove.
Model 1: Y = + Î²

_{0}+ Î²_{1}X_{1}+ Î²_{2}X_{2}+ Î²_{3}X_{1}X_{2 }+ Î²_{4}_{ }+ Î²_{S}_{ }X_{2}+ Îµ where Y = Sale price of property in thousands of dollars X_{1}= Size of property in thousands of square feet X_{2}= 1 if property located near cove,0 if not Using the data collected for the 20 properties,the following partial output obtained from Microsoft Excel is shown: -Referring to Table 15-2,given a quadratic relationship between sale price (Y)and property size (X_{1}),what test should be used to test whether the curves differ from cove and non-cove properties?Free

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Multiple Choice

Q17 Q17 Q17

As a project for his business statistics class,a student examined the factors that determined parking meter rates throughout the campus area.Data were collected for the price per hour of parking,blocks to the quadrangle,and one of the three jurisdictions: on campus,in downtown and off campus,or outside of downtown and off campus.The population regression model hypothesized is Y = Î²

_{0}+ Î²_{1}X_{1}_{i}+ Î²_{2}X_{2}_{i}+ Î²_{3}X_{3}_{i}+ Îµ Where Y is the meter price X_{1}is the number of blocks to the quad X_{2}is a dummy variable that takes the value 1 if the meter is located in downtown and off campus and the value 0 otherwise X_{3}is a dummy variable that takes the value 1 if the meter is located outside of downtown and off campus,and the value 0 otherwise Suppose that whether the meter is located on campus is an important explanatory factor.Why should the variable that depicts this attribute not be included in the model?Free

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Q27 Q27 Q27

True or False: Two simple regression models were used to predict a single dependent variable.Both models were highly significant,but when the two independent variables were placed in the same multiple regression model for the dependent variable,R

^{2}did not increase substantially and the parameter estimates for the model were not significantly different from 0.This is probably an example of collinearity.Free

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True False

Q29 Q29 Q29

TABLE 15-3
A chemist employed by a pharmaceutical firm has developed a muscle relaxant.She took a sample of 14 people suffering from extreme muscle constriction.She gave each a vial containing a dose (X)of the drug and recorded the time to relief (Y)measured in seconds for each.She fit a curvilinear model to this data.The results obtained by Microsoft Excel follow
-Referring to Table 15-3,the prediction of time to relief for a person receiving a dose of 10 units of the drug is ________.

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Short Answer

Q30 Q30 Q30

TABLE 15-3
A chemist employed by a pharmaceutical firm has developed a muscle relaxant.She took a sample of 14 people suffering from extreme muscle constriction.She gave each a vial containing a dose (X)of the drug and recorded the time to relief (Y)measured in seconds for each.She fit a curvilinear model to this data.The results obtained by Microsoft Excel follow
-Referring to Table 15-3,suppose the chemist decides to use an F test to determine if there is a significant curvilinear relationship between time and dose.The p-value of the test is ________.

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Short Answer

Q31 Q31 Q31

TABLE 15-3
A chemist employed by a pharmaceutical firm has developed a muscle relaxant.She took a sample of 14 people suffering from extreme muscle constriction.She gave each a vial containing a dose (X)of the drug and recorded the time to relief (Y)measured in seconds for each.She fit a curvilinear model to this data.The results obtained by Microsoft Excel follow
-Referring to Table 15-3,suppose the chemist decides to use an F test to determine if there is a significant curvilinear relationship between time and dose.The value of the test statistic is ________.

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Short Answer

Q32 Q32 Q32

True or False: Referring to Table 15-3,suppose the chemist decides to use an F test to determine if there is a significant curvilinear relationship between time and dose.If she chooses to use a level of significance of 0.05,she would decide that there is a significant curvilinear relationship.

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True False

Q33 Q33 Q33

True or False: Referring to Table 15-3,suppose the chemist decides to use an F test to determine if there is a significant curvilinear relationship between time and dose.If she chooses to use a level of significance of 0.01 she would decide that there is a significant curvilinear relationship.

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True False

Q34 Q34 Q34

Referring to Table 15-3,suppose the chemist decides to use a t test to determine if there is a significant difference between a linear model and a curvilinear model that includes a linear term.The p-value of the test statistic for the contribution of the curvilinear term is ________.

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Q37 Q37 Q37

True or False: Referring to Table 15-3,suppose the chemist decides to use a t test to determine if there is a significant difference between a linear model and a curvilinear model that includes a linear term.If she used a level of significance of 0.05,she would decide that the linear model is sufficient.

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True False

Q38 Q38 Q38

True or False: Referring to Table 15-3,suppose the chemist decides to use a t test to determine if there is a significant difference between a linear model and a curvilinear model that includes a linear term.If she used a level of significance of 0.01,she would decide that the linear model is sufficient.

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Q53 Q53 Q53

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -Referring to Table 15-4,what are,respectively,the values of the variance inflationary factor of the 3 predictors?Free

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Q54 Q54 Q54

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -True or False: Referring to Table 15-4,there is reason to suspect collinearity between some pairs of predictors.Free

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True False

Q55 Q55 Q55

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -Referring to Table 15-4,which of the following predictors should first be dropped to remove collinearity?Free

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Q56 Q56 Q56

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -Referring to Table 15-4,which of the following models should be taken into consideration using the Mallows' C_{p}statistic?Free

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Q57 Q57 Q57

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -Referring to Table 15-4,the "best" model using a 5% level of significance among those chosen by the C_{p}statistic isFree

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Multiple Choice

Q58 Q58 Q58

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -Referring to Table 15-4,the "best" model chosen using the adjusted R-square statistic isFree

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Multiple Choice

Q59 Q59 Q59

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -Referring to Table 15-4,the better model using a 5% level of significance derived from the "best" model above isFree

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Q60 Q60 Q60

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -True or False: Referring to Table 15-4,the residual plot suggests that a nonlinear model on % attendance may be a better model.Free

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True False

Q61 Q61 Q61

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -Referring to Table 15-4,what is the value of the test statistic to determine whether the quadratic effect of daily average of the percentage of students attending class on percentage of students passing the proficiency test is significant at a 5% level of significance?Free

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Q62 Q62 Q62

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -Referring to Table 15-4,what is the p-value of the test statistic to determine whether the quadratic effect of daily average of the percentage of students attending class on percentage of students passing the proficiency test is significant at a 5% level of significance?Free

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Q63 Q63 Q63

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -True or False: Referring to Table 15-4,the null hypothesis should be rejected when testing whether the quadratic effect of daily average of the percentage of students attending class on percentage of students passing the proficiency test is significant at a 5% level of significance.Free

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True False

Q64 Q64 Q64

TABLE 15-4
The superintendent of a school district wanted to predict the percentage of students passing a sixth-grade proficiency test.She obtained the data on percentage of students passing the proficiency test (% Passing),daily mean of the percentage of students attending class (% Attendance),mean teacher salary in dollars (Salaries),and instructional spending per pupil in dollars (Spending)of 47 schools in the state.
Let Y = % Passing as the dependent variable,X

_{1}= % Attendance,X_{2}= Salaries and X_{3}= Spending. The coefficient of multiple determination ( )of each of the 3 predictors with all the other remaining predictors are,respectively,0.0338,0.4669,and 0.4743. The output from the best-subset regressions is given below: Following is the residual plot for % Attendance: Following is the output of several multiple regression models: Model (I): Model (II): Model (III): -True or False: Referring to Table 15-4,the quadratic effect of daily average of the percentage of students attending class on percentage of students passing the proficiency test is not significant at a 5% level of significance.Free

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True False

Q65 Q65 Q65

TABLE 15-5
What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 171 different vehicle models were collected:
Accel Time: Acceleration time in sec.
Cargo Vol: Cargo volume in cu.ft.
HP: Horsepower
MPG: Miles per gallon
SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0
Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0
The coefficient of multiple determination ( )for the regression model using each of the 5 variables X

_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.7461,0.5676,0.6764,0.8582,0.6632. -Referring to Table 15-5,what is the value of the variance inflationary factor of Cargo Vol?Free

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Short Answer

Q66 Q66 Q66

TABLE 15-5
What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 171 different vehicle models were collected:
Accel Time: Acceleration time in sec.
Cargo Vol: Cargo volume in cu.ft.
HP: Horsepower
MPG: Miles per gallon
SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0
Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0
The coefficient of multiple determination ( )for the regression model using each of the 5 variables X

_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.7461,0.5676,0.6764,0.8582,0.6632. -Referring to Table 15-5,what is the value of the variance inflationary factor of HP?Free

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Short Answer

Q67 Q67 Q67

TABLE 15-5
What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 171 different vehicle models were collected:
Accel Time: Acceleration time in sec.
Cargo Vol: Cargo volume in cu.ft.
HP: Horsepower
MPG: Miles per gallon
SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0
Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0
The coefficient of multiple determination ( )for the regression model using each of the 5 variables X

_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.7461,0.5676,0.6764,0.8582,0.6632. -Referring to Table 15-5,what is the value of the variance inflationary factor of MPG?Free

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Short Answer

Q68 Q68 Q68

TABLE 15-5
What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 171 different vehicle models were collected:
Accel Time: Acceleration time in sec.
Cargo Vol: Cargo volume in cu.ft.
HP: Horsepower
MPG: Miles per gallon
SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0
Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0
The coefficient of multiple determination ( )for the regression model using each of the 5 variables X

_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.7461,0.5676,0.6764,0.8582,0.6632. -Referring to Table 15-5,what is the value of the variance inflationary factor of SUV?Free

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Short Answer

Q69 Q69 Q69

TABLE 15-5
What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 171 different vehicle models were collected:
Accel Time: Acceleration time in sec.
Cargo Vol: Cargo volume in cu.ft.
HP: Horsepower
MPG: Miles per gallon
SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0
Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0
The coefficient of multiple determination ( )for the regression model using each of the 5 variables X

_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.7461,0.5676,0.6764,0.8582,0.6632. -Referring to Table 15-5,what is the value of the variance inflationary factor of Sedan?Free

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Short Answer

Q70 Q70 Q70

TABLE 15-5
What are the factors that determine the acceleration time (in sec.)from 0 to 60 miles per hour of a car? Data on the following variables for 171 different vehicle models were collected:
Accel Time: Acceleration time in sec.
Cargo Vol: Cargo volume in cu.ft.
HP: Horsepower
MPG: Miles per gallon
SUV: 1 if the vehicle model is an SUV with Coupe as the base when SUV and Sedan are both 0
Sedan: 1 if the vehicle model is a sedan with Coupe as the base when SUV and Sedan are both 0
The coefficient of multiple determination ( )for the regression model using each of the 5 variables X

_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.7461,0.5676,0.6764,0.8582,0.6632. -True or False: Referring to Table 15-5,there is reason to suspect collinearity between some pairs of predictors based on the values of the variance inflationary factor.Free

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True False

Q71 Q71 Q71

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the variance inflationary factor of Age?Free

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Q72 Q72 Q72

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the variance inflationary factor of Edu?Free

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Q73 Q73 Q73

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the variance inflationary factor of Job Yr?Free

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Q74 Q74 Q74

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the variance inflationary factor of Married?Free

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Q75 Q75 Q75

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the variance inflationary factor of Head?Free

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Q76 Q76 Q76

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the variance inflationary factor of Manager?Free

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Q77 Q77 Q77

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,there is reason to suspect collinearity between some pairs of predictors based on the values of the variance inflationary factor.Free

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True False

Q78 Q78 Q78

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the variable X_{1}should be dropped to remove collinearity.Free

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True False

Q79 Q79 Q79

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the variable X_{2}should be dropped to remove collinearity.Free

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True False

Q80 Q80 Q80

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the variable X_{3 }should be dropped to remove collinearity.Free

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True False

Q81 Q81 Q81

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the variable X_{4 }should be dropped to remove collinearity.Free

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True False

Q82 Q82 Q82

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the variable X_{5 }should be dropped to remove collinearity.Free

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True False

Q83 Q83 Q83

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the variable X_{6 }should be dropped to remove collinearity.Free

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True False

Q84 Q84 Q84

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the Mallow's C_{p}statistic for the model that includes X_{1},X_{5 }and X_{6}?Free

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Q85 Q85 Q85

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the Mallow's C_{p}statistic for the model that includes X_{1},X_{2},X_{5}and X_{6}?Free

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Q86 Q86 Q86

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the Mallow's C_{p}statistic for the model that includes X_{1},X_{3},X_{5}and X_{6}?Free

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Q87 Q87 Q87

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the Mallow's C_{p}statistic for the model that includes X_{1},X_{2},X_{3},X_{5}and X_{6}?Free

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Q88 Q88 Q88

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -Referring to Table 15-6,what is the value of the Mallow's C_{p}statistic for the model that includes all the six independent variables?Free

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Q89 Q89 Q89

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the model that includes X_{1},X_{5}and X_{6 }should be among the appropriate models using the Mallow's C_{p}statistic.Free

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True False

Q90 Q90 Q90

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the model that includes X_{1},X_{2},X_{5}and X_{6 }should be among the appropriate models using the Mallow's C_{p}statistic.Free

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True False

Q91 Q91 Q91

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the model that includes X_{1},X_{3},X_{5}and X_{6 }should be among the appropriate models using the Mallow's C_{p}statistic.Free

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True False

Q92 Q92 Q92

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the model that includes X_{1},X_{2},X_{3},X_{5}and X_{6 }should be among the appropriate models using the Mallow's C_{p}statistic.Free

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True False

Q93 Q93 Q93

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the model that includes all the six independent variables_{ }should be among the appropriate models using the Mallow's C_{p}statistic.Free

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True False

Q94 Q94 Q94

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the model that includes all six independent variables_{ }should be selected using the adjusted r^{2}statistic.Free

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True False

Q95 Q95 Q95

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the model that includes X_{1},X_{2},X_{3},X_{5}and X_{6 }should be selected using the adjusted r^{2}statistic.Free

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True False

Q96 Q96 Q96

TABLE 15-6
Given below are results from the regression analysis on 40 observations where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Y)and the independent variables are the age of the worker (X

_{1}),the number of years of education received (X_{2}),the number of years at the previous job (X_{3}),a dummy variable for marital status (X_{4}: 1 = married,0 = otherwise),a dummy variable for head of household (X_{5}: 1 = yes,0 = no)and a dummy variable for management position (X_{6}: 1 = yes,0 = no). The coefficient of multiple determination ( )for the regression model using each of the 6 variables X_{j}as the dependent variable and all other X variables as independent variables are,respectively,0.2628,0.1240,0.2404,0.3510,0.3342 and 0.0993. The partial results from best-subset regression are given below: -True or False: Referring to Table 15-6,the model that includes X_{1},X_{5}and X_{6 }should be selected using the adjusted r^{2}statistic.Free

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True False