Exam 15: Multiple Regression Model Building

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In multiple regression, the ________ procedure permits variables to enter and leave the model at different stages of its development.

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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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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: 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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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 Cp statistic for the model that includes X₁, X₂, X₃, X₅ and X₆? -Referring to Table 15-6, what is the value of the Mallow's Cp statistic for the model that includes X₁, X₂, X₃, X₅ and X₆?

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In stepwise regression, an independent variable is not allowed to be removed from the model once it has entered into the model.

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TABLE 15-4 TABLE 15-4     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' Cp statistic? The output from the best-subset regressions is given below: TABLE 15-4     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' Cp statistic? Following is the residual plot for % Attendance: TABLE 15-4     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' Cp statistic? Following is the output of several multiple regression models: Model (I): TABLE 15-4     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' Cp statistic? Model (II): TABLE 15-4     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' Cp statistic? Model (III): TABLE 15-4     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' Cp statistic? -Referring to Table 15-4, which of the following models should be taken into consideration using the Mallows' Cp statistic?

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TABLE 15-4 TABLE 15-4     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 Cp statistic is The output from the best-subset regressions is given below: TABLE 15-4     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 Cp statistic is Following is the residual plot for % Attendance: TABLE 15-4     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 Cp statistic is Following is the output of several multiple regression models: Model (I): TABLE 15-4     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 Cp statistic is Model (II): TABLE 15-4     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 Cp statistic is Model (III): TABLE 15-4     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 Cp statistic is -Referring to Table 15-4, the "best" model using a 5% level of significance among those chosen by the Cp statistic is

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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 "centered" curvilinear model to this data. The results obtained by Microsoft Excel follow, where the dose (X) given has been "centered." 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 centered curvilinear model to this data. The results obtained by Microsoft Excel follow, where the dose (X) given has been centered.    -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. -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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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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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: 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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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, the model that includes X₁, X₅ and X₆ should be among the appropriate models using the Mallow's Cp statistic. -Referring to Table 15-6, the model that includes X₁, X₅ and X₆ should be among the appropriate models using the Mallow's Cp statistic.

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Collinearity is present when there is a high degree of correlation between the dependent variable and any of the independent variables.

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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 (R) for the regression model using each of the 5 variables Xⱼ 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?

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One of the consequences of collinearity in multiple regression is biased estimates on the slope coefficients.

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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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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: 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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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, the model that includes X₁, X₃, X₅ and X₆ should be among the appropriate models using the Mallow's Cp statistic. -Referring to Table 15-6, the model that includes X₁, X₃, X₅ and X₆ should be among the appropriate models using the Mallow's Cp statistic.

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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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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: 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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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, the model that includes X₁, X₅ and X₆ should be selected using the adjusted r² statistic. -Referring to Table 15-6, the model that includes X₁, X₅ and X₆ should be selected using the adjusted r² statistic.

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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 "centered" curvilinear model to this data. The results obtained by Microsoft Excel follow, where the dose (X) given has been "centered." 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 centered curvilinear model to this data. The results obtained by Microsoft Excel follow, where the dose (X) given has been centered.    -Referring to Table 15-3, suppose the chemist decides to use a t test to determine if the linear term is significant. The value of the test statistic is ________. -Referring to Table 15-3, suppose the chemist decides to use a t test to determine if the linear term is significant. The value of the test statistic is ________.

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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 (R) for the regression model using each of the 5 variables Xⱼ 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?

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A regression diagnostic tool used to study the possible effects of collinearity is

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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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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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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: 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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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, the model that includes X₁, X₂, X₅ and X₆ should be among the appropriate models using the Mallow's Cp statistic. -Referring to Table 15-6, the model that includes X₁, X₂, X₅ and X₆ should be among the appropriate models using the Mallow's Cp statistic.

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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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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: 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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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? -Referring to Table 15-6, what is the value of the variance inflationary factor of Married?

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TABLE 15-4 TABLE 15-4     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 is The output from the best-subset regressions is given below: TABLE 15-4     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 is Following is the residual plot for % Attendance: TABLE 15-4     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 is Following is the output of several multiple regression models: Model (I): TABLE 15-4     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 is Model (II): TABLE 15-4     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 is Model (III): TABLE 15-4     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 is -Referring to Table 15-4, the better model using a 5% level of significance derived from the "best" model above is

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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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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: 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 (X₁), the number of years of education received (X₂), the number of years at the previous job (X₃), a dummy variable for marital status (X₄: 1 = married, 0 = otherwise), a dummy variable for head of household (X₅: 1 = yes, 0 = no) and a dummy variable for management position (X₆: 1 = yes, 0 = no). The coefficient of multiple determination (R) for the regression model using each of the 6 variables Xⱼ 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 Cp statistic for the model that includes X₁, X₅ and X₆? -Referring to Table 15-6, what is the value of the Mallow's Cp statistic for the model that includes X₁, X₅ and X₆?

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