Exam 16: Regression Analysis: Model Building

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Exhibit 16-4 In a laboratory experiment,data were gathered on the life span (Y in months)of 33 rats,units of daily protein intake (X1),and whether or not agent X2 (a proposed life extending agent)was added to the rats diet (X2 = 0 if agent X2 was not added,and X2 = 1 if agent was added. )From the results of the experiment,the following regression model was developed. Exhibit 16-4 In a laboratory experiment,data were gathered on the life span (Y in months)of 33 rats,units of daily protein intake (X<sub>1</sub>),and whether or not agent X<sub>2</sub> (a proposed life extending agent)was added to the rats diet (X<sub>2</sub> = 0 if agent X<sub>2</sub> was not added,and X<sub>2</sub> = 1 if agent was added. )From the results of the experiment,the following regression model was developed.   Also provided are SSR = 60 and SST = 180. -Refer to Exhibit 16-4.The test statistic for testing the significance of the model is Also provided are SSR = 60 and SST = 180. -Refer to Exhibit 16-4.The test statistic for testing the significance of the model is

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A model in the form of y = β\beta 0 + β\beta 1z1 + β\beta 2z2 + ...+ β\beta pzp + ε\varepsilon where each independent variable zj (for j = 1,2,... ,p)is a function of xj .xj is known as the

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Exhibit 16-4 In a laboratory experiment,data were gathered on the life span (Y in months)of 33 rats,units of daily protein intake (X1),and whether or not agent X2 (a proposed life extending agent)was added to the rats diet (X2 = 0 if agent X2 was not added,and X2 = 1 if agent was added. )From the results of the experiment,the following regression model was developed. Exhibit 16-4 In a laboratory experiment,data were gathered on the life span (Y in months)of 33 rats,units of daily protein intake (X<sub>1</sub>),and whether or not agent X<sub>2</sub> (a proposed life extending agent)was added to the rats diet (X<sub>2</sub> = 0 if agent X<sub>2</sub> was not added,and X<sub>2</sub> = 1 if agent was added. )From the results of the experiment,the following regression model was developed.   Also provided are SSR = 60 and SST = 180. -Refer to Exhibit 16-4.The life expectancy of a rat that was not given any protein and that did not take agent X<sub>2</sub> is Also provided are SSR = 60 and SST = 180. -Refer to Exhibit 16-4.The life expectancy of a rat that was not given any protein and that did not take agent X2 is

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Multiple regression analysis was used to study the relationship between a dependent variable,Y,and three independent variables X1,X2 and,X3.The following is a partial result of the regression analysis involving 20 observations.  Multiple regression analysis was used to study the relationship between a dependent variable,Y,and three independent variables X<sub>1</sub>,X<sub>2</sub> and,X<sub>3</sub>.The following is a partial result of the regression analysis involving 20 observations.     a.Compute the coefficient of determination. b.Perform a t test and determine whether or not  \beta <sub>1</sub>is significantly different from zero ( \alpha  = 0.05). c.Perform a t test and determine whether or not  \beta <sub>2</sub> is significantly different from zero ( \alpha  = 0.05). d.Perform a t test and determine whether or not  \beta <sub>3</sub> is significantly different from zero ( \alpha  = 0.05). e.At \9\alpha\) = 0.05,perform an F test and determine whether or not the regression model is significant. a.Compute the coefficient of determination. b.Perform a t test and determine whether or not β\beta 1is significantly different from zero ( α\alpha = 0.05). c.Perform a t test and determine whether or not β\beta 2 is significantly different from zero ( α\alpha = 0.05). d.Perform a t test and determine whether or not β\beta 3 is significantly different from zero ( α\alpha = 0.05). e.At \9\alpha\) = 0.05,perform an F test and determine whether or not the regression model is significant.

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Monthly total production costs and the number of units produced at a local company over a period of 10 months are shown below. Monthly total production costs and the number of units produced at a local company over a period of 10 months are shown below.     a.Draw a scatter diagram for the above data. b.Assume that a model in the form of best describes the relationship between X and Y.Estimate the parameters of this curvilinear regression equation. a.Draw a scatter diagram for the above data. b.Assume that a model in the form of best describes the relationship between X and Y.Estimate the parameters of this curvilinear regression equation.

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A regression model relating a dependent variable,Y,with one independent variable,X1,resulted in an SSE of 400.Another regression model with the same dependent variable,Y,and two independent variables,X1 and X2,resulted in an SSE of 320.At α\alpha = .05,determine if X2 contributed significantly to the model.The sample size for both models was 20.

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A data set consisting of 7 observations of a dependent variable y and two independent variables x1 and x2 was used in a regression analysis.Using (x1)as the only independent variable,the following function is provided.  A data set consisting of 7 observations of a dependent variable y and two independent variables x<sub>1</sub> and x<sub>2</sub> was used in a regression analysis.Using (x<sub>1</sub>)as the only independent variable,the following function is provided.    = 0.408 + 1.338x<sub>1</sub> The SSE for the above model is 39.535. Using both x<sub>1</sub> and x<sub>2</sub> as independent variables yields the following function.    = 0.805 + 0.498x<sub>1</sub> - 0.477x<sub>2</sub> The SSE for this latter function is 1.015. Use an F test and determine if x<sub>2</sub> contributes significantly to the model.Let  \alpha  = 0.05. = 0.408 + 1.338x1 The SSE for the above model is 39.535. Using both x1 and x2 as independent variables yields the following function.  A data set consisting of 7 observations of a dependent variable y and two independent variables x<sub>1</sub> and x<sub>2</sub> was used in a regression analysis.Using (x<sub>1</sub>)as the only independent variable,the following function is provided.    = 0.408 + 1.338x<sub>1</sub> The SSE for the above model is 39.535. Using both x<sub>1</sub> and x<sub>2</sub> as independent variables yields the following function.    = 0.805 + 0.498x<sub>1</sub> - 0.477x<sub>2</sub> The SSE for this latter function is 1.015. Use an F test and determine if x<sub>2</sub> contributes significantly to the model.Let  \alpha  = 0.05. = 0.805 + 0.498x1 - 0.477x2 The SSE for this latter function is 1.015. Use an F test and determine if x2 contributes significantly to the model.Let α\alpha = 0.05.

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When dealing with the problem of non-constant variance,the reciprocal transformation means using

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Exhibit 16-4 In a laboratory experiment,data were gathered on the life span (Y in months)of 33 rats,units of daily protein intake (X1),and whether or not agent X2 (a proposed life extending agent)was added to the rats diet (X2 = 0 if agent X2 was not added,and X2 = 1 if agent was added. )From the results of the experiment,the following regression model was developed. Exhibit 16-4 In a laboratory experiment,data were gathered on the life span (Y in months)of 33 rats,units of daily protein intake (X<sub>1</sub>),and whether or not agent X<sub>2</sub> (a proposed life extending agent)was added to the rats diet (X<sub>2</sub> = 0 if agent X<sub>2</sub> was not added,and X<sub>2</sub> = 1 if agent was added. )From the results of the experiment,the following regression model was developed.   Also provided are SSR = 60 and SST = 180. -Refer to Exhibit 16-4.From the above function,it can be said that the life expectancy of rats that were given agent X<sub>2</sub> is Also provided are SSR = 60 and SST = 180. -Refer to Exhibit 16-4.From the above function,it can be said that the life expectancy of rats that were given agent X2 is

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Consider the following data for two variables x and y.  Consider the following data for two variables x and y.     a.An estimated regression equation of the formwas developed for the above data and the results are shown below.Comment on the adequacy of this equation for predicting y.Let  \alpha  = .05.    b.A regression equation of the formwas developed for the above data and results are shown below.Comment on the adequacy of this equation for predicting y.Let  \alpha  = .05.    c.Predict the value of y when x = 5. a.An estimated regression equation of the formwas developed for the above data and the results are shown below.Comment on the adequacy of this equation for predicting y.Let α\alpha = .05.  Consider the following data for two variables x and y.     a.An estimated regression equation of the formwas developed for the above data and the results are shown below.Comment on the adequacy of this equation for predicting y.Let  \alpha  = .05.    b.A regression equation of the formwas developed for the above data and results are shown below.Comment on the adequacy of this equation for predicting y.Let  \alpha  = .05.    c.Predict the value of y when x = 5. b.A regression equation of the formwas developed for the above data and results are shown below.Comment on the adequacy of this equation for predicting y.Let α\alpha = .05.  Consider the following data for two variables x and y.     a.An estimated regression equation of the formwas developed for the above data and the results are shown below.Comment on the adequacy of this equation for predicting y.Let  \alpha  = .05.    b.A regression equation of the formwas developed for the above data and results are shown below.Comment on the adequacy of this equation for predicting y.Let  \alpha  = .05.    c.Predict the value of y when x = 5. c.Predict the value of y when x = 5.

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A test to determine whether or not first-order autocorrelation is present is

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A regression analysis was applied in order to determine the relationship between a dependent variable and 4 independent variables.The following information was obtained from the regression analysis.  A regression analysis was applied in order to determine the relationship between a dependent variable and 4 independent variables.The following information was obtained from the regression analysis.     a.Fill in the blanks in the following ANOVA table. b.At  \alpha = 0.05 level of significance,test to determine if the model is significant.   a.Fill in the blanks in the following ANOVA table. b.At α\alpha = 0.05 level of significance,test to determine if the model is significant.  A regression analysis was applied in order to determine the relationship between a dependent variable and 4 independent variables.The following information was obtained from the regression analysis.     a.Fill in the blanks in the following ANOVA table. b.At  \alpha = 0.05 level of significance,test to determine if the model is significant.

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Exhibit 16-1 In a regression analysis involving 25 observations,the following estimated regression equation was developed. Exhibit 16-1 In a regression analysis involving 25 observations,the following estimated regression equation was developed.   Also,the following standard errors and the sum of squares were obtained.    -Refer to Exhibit 16-1.The coefficient of X<sub>1</sub> Also,the following standard errors and the sum of squares were obtained. Exhibit 16-1 In a regression analysis involving 25 observations,the following estimated regression equation was developed.   Also,the following standard errors and the sum of squares were obtained.    -Refer to Exhibit 16-1.The coefficient of X<sub>1</sub> -Refer to Exhibit 16-1.The coefficient of X1

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What value of Durbin-Watson statistic indicates no autocorrelation is present?

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The parameters of nonlinear models have exponents

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