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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 11503 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 11504 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 11505 = .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 11506 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 11507 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 11508 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 bypass surgery among the four hospitals. The response variable is the amount of time
spent in the hospital (in days), t...
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 Winter '14

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