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MSE is equal to 144 and the leverage value is 0.19. Calculate the deleted residual for this
observation. 93. In a multiple regression model with 4 independent variables and 25 observations, an
observation's actual y value is 128.2, and the predicted value of the dependent variable based
on the multiple regression equation is equal to 114.7. The computer output also shows that
MSE is equal to 144 and the leverage value is 0.19. Calculate the studentized deleted residual
for this observation. 11471 Chapter 01  An Introduction to Business Statistics 94. In a multiple regression model with 4 independent variables and 25 observations, an
observation's actual y value is 128.2, and the predicted value of the dependent variable based
on the multiple regression equation is equal to 114.7. The computer output also shows that
MSE is equal to 144 and the leverage value is 0.19. Calculate the adjusted R2 where R2 = .
716, the number of observations is 20, and the number of independent variables is 2. 95. In a multiple regression model with 4 independent variables and 25 observations, an
observation's actual y value is 128.2, and the predicted value of the dependent variable based
on the multiple regression equation is equal to 114.7. The computer output also shows that
MSE is equal to 144 and the leverage value is 0.19. Calculate the Cook's D statistic for this
observation. Based on the Cook's statistic value is this observation influential? Why? 11472 Chapter 01  An Introduction to Business Statistics 96. The following time sequenced observations of actual and predicted values of the
dependent variable (demand) are obtained from a multiple regression model with 3
independent variables. Determine the DurbinWatson statistic (d). 97. Based on 25 time ordered observations from a multiple regression model with 3
independent variables, we have determined the DurbinWatson statistic, d = 1.29. At α = .05,
test to determine if there is any evidence of positive autocorrelation? State your conclusions. 98. Based on 30 time ordered observations from a multiple regression model with 2
independent variables, we have determined the DurbinWatson statistic, d = 2.71. At α = .05,
test to determine if there is any evidence of negative autocorrelation? State your conclusions. 11473 Chapter 01  An Introduction to Business Statistics 99. A multiple regression model was applied to a data set with 8 time ordered observations.
The residuals for these observations are given below. Calculate the value of MSE and the standard error. 100. A multiple regression model was applied to a data set with 8 time ordered observations.
The residuals for these observations are given below. Calculate the DurbinWatson statistic (d). 11474 Chapter 01  An Introduction to Business Statistics 101. A multiple regression model with 3 independent variables and 16 observations produced
the following analysis of variance table. After some consideration, the analyst decided to add two new independent variables to the
multiple regression model. As a result of this change, the unexplained variation (SSE)
decreased by 5. Complete the new analysis of variance table after the addition of the two new
independent variables. Did the two newly added variables improve the model? 102. Use the following correlation matrix and determine the best multiple regression
prediction equation that has no significant multicollinearity. 11475 Chapter 01  An Introduction to Business Statistics Chapter 15 Model Building and Model Diagnostics Answer Key True / False Questions 1. If we increase the number of independent variables in a multiple regression model, the F
statistic will always increase.
FALSE AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Hard
Learning Objective: 5
Topic: Model Building 2. When the quadratic regression model y =
shows the rate of curvature of the parabola.
TRUE 0 + AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Hard
Learning Objective: 1
Topic: Quadratic 11476 x+ 1 x2 + ε is used the term 2 1 Chapter 01  An Introduction to Business Statistics 3. The variance inflation factor measures the relationship between the dependent variable and
the rest of the independent variables in the regression model.
FALSE AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Medium
Learning Objective: 5
Topic: Model Building 4. Even when an unimportant variable is added to a regression model, the explained variation
will increase.
TRUE AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Medium
Learning Objective: 5
Topic: Model Building 11477 Chapter 01  An Introduction to Business Statistics 5. The standard error decreases if and only if the adjusted multiple coefficient of
determination decreases.
FALSE AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Hard
Learning Objective: 5
Topic: Model Building 6. One way to identify outliers with respect to their x values is to calculate their leverage
values.
TRUE AACSB: Reflect...
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 Winter '14

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