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A c b f c t d vif aacsb reflective thinking blooms

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Unformatted text preview: cision. F = 3.68, reject H0 AACSB: Analytical Skills Bloom's: Application Difficulty: Hard Learning Objective: 1 Topic: Quadratic 1-1503 Chapter 01 - An Introduction to Business Statistics 78. Below is a partial multiple regression computer output based on a quadratic regression model. Assuming a quadratic model was used, write the least squares prediction equation. = 8.01 - 1.35X + .46X2 AACSB: Analytical Skills Bloom's: Application Difficulty: Medium Learning Objective: 1 Topic: Quadratic 1-1504 Chapter 01 - An Introduction to Business Statistics 79. Below is a partial multiple regression computer output based on a quadratic regression model. Test the usefulness of the variable X2 in the model at t = -2.45, reject H0 AACSB: Analytical Skills Bloom's: Application Difficulty: Medium Learning Objective: 1 Topic: Quadratic 1-1505 = .05. Chapter 01 - An Introduction to Business Statistics 80. Below is a partial multiple regression computer output based on a quadratic regression model to predict student enrollment at a local university. The dependent variable is the annual enrollment given in thousands of students, the independent variable X is the increase in tuition stated in thousands of dollars per year, and X2 is the square of tuition increase given in squared thousands of dollars per year. Interpret β0 (the y intercept) and β1 (the β coefficient for the X variable). Does the parabola open upward or downward? Why? The interpretation of the y intercept: If there is no increase in tuition, the estimated enrollment is an average of 8,010 students per year. The interpretation of the β coefficient for the X variable: For each additional thousand dollar of increase in tuition, the expected decrease in enrollment is 1350 students per year. Since the value of β2 is positive, the parabola opens upward. AACSB: Analytical Skills Bloom's: Application Difficulty: Medium Learning Objective: 1 Topic: Quadratic 1-1506 Chapter 01 - An Introduction to Business Statistics 81. A multiple linear regression analysis involving 45 observations resulted in the following least squares prediction equation: . The SSE for the above model is 49. Addition of two other independent variables to the model, resulted in the following multiple linear regression equation: . The latter model's SSE is 40. Determine the degrees of freedom regression, degrees of freedom error, and degrees of freedom total for the model with two independent variables. dfR = 2, dfE = 42, dftotal = 44 AACSB: Analytical Skills Bloom's: Application Difficulty: Medium Learning Objective: 5 Topic: Model Building 82. A multiple linear regression analysis involving 45 observations resulted in the following least squares prediction equation: . The SSE for the above model is 49. Addition of two other independent variables to the model, resulted in the following multiple linear regression equation: . The latter model's SSE is 40. Determine the degrees of freedom regression, degrees of freedom error, and degrees of freedom total for the latter model (the model with four independent variables). dfR = 4, dfE = 40, dftotal = 44 AACSB: Analytical Skills Bloom's: Application Difficulty: Medium Learning Objective: 5 Topic: Model Building 1-1507 Chapter 01 - An Introduction to Business Statistics 83. A multiple linear regression analysis involving 45 observations resulted in the following least squares prediction equation: . The SSE for the above model is 49. Addition of two other independent variables to the model, resulted in the following multiple linear regression equation: . The latter model's SSE is 40. The analyst performing the study wants to determine if at least one of the two new independent variables makes a significant contribution to the multiple regression model. State the appropriate null and alternative hypotheses. H0: β3 = β4 = 0 HA: At least one of β3, β4 ≠ 0 AACSB: Reflective Thinking Bloom's: Knowledge Difficulty: Hard Learning Objective: 5 Topic: Model Building 84. A multiple linear regression analysis involving 45 observations resulted in the following least squares prediction equation: . The SSE for the above model is 49. Addition of two other independent variables to the model, resulted in the following multiple linear regression equation: . The latter model's SSE is 40. At = .05 test to determine if at least one of the two new independent variables make a significant contribution to the multiple regression model. Reject H0. At least one of the two newly added variables makes a significant contribution in predicting the dependent variable. AACSB: Analytical Skills Bloom's: Application Difficulty: Hard Learning Objective: 5 Topic: Model Building 1-1508 Chapter 01 - An Introduction to Business Statistics 85. A county has four major hospitals: 1) Regional Memorial; 2) General; 3) Charity; and 4) City. A multiple regression model is used to compare the time spent in the hospital after a heart by-pass surgery among the four hospitals. The response variable is the amount of time spent in the hospital (in days), t...
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