Unformatted text preview: 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.
.683 AACSB: Analytical Skills
Bloom's: Application
Difficulty: Medium
Learning Objective: 5
Topic: Model Building 11513 Chapter 01  An Introduction to Business Statistics 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?
0.0733. No, this observation is not influential because .0733 < 1 AACSB: Analytical Skills
Bloom's: Application
Difficulty: Hard
Learning Objective: 5
Topic: Outliers & Influential Observations 11514 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). 1.889 AACSB: Analytical Skills
Bloom's: Application
Difficulty: Medium
Learning Objective: 8
Topic: Autocorrelation 11515 Chapter 01  An Introduction to Business Statistics 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.
No because 1.12 < 1.29 < 1.66. The test results are inconclusive. AACSB: Analytical Skills
Bloom's: Application
Difficulty: Medium
Learning Objective: 8
Topic: Autocorrelation 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.
Yes, there is evidence of negative autocorrelation because 2.71 > 2.65. AACSB: Analytical Skills
Bloom's: Application
Difficulty: Medium
Learning Objective: 8
Topic: Autocorrelation 11516 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.
0.4719, .6869 AACSB: Analytical Skills
Bloom's: Application
Difficulty: Medium
Learning Objective: 5
Topic: Model Building 11517 Chapter 01  An Introduction to Business Statistics 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).
2.055 AACSB: Analytical Skills
Bloom's: Application
Difficulty: Medium
Learning Objective: 8
Topic: Autocorrelation 11518 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?
No. The F value decreased from 8 to 7. AACSB: Analytical Skills
Bloom's: Application
Difficulty: Hard
Learning Objective: 5
Topic: Model Building 102. Use the following correlation matrix and determine the best multiple regression
prediction equation that has no significant multicollinearity. AACSB: Analytical Skills
Bloom's: Application
Difficulty: Hard
Learning Objective: 4
Topic: Multicollinearity Chapter 16
Time Series Forecasting 11519 Chapter 01  An Introduction to Business Statistics
True / False Questions 1. A univariate time series model is used to predict future values of a time series based only
upon past values of a time series.
True False 2. Trend refers to a longrun upward or downward movement of a time series over a period of
time.
True False 3. Exponential smoothing is a forecasting method that applies equal weights to the time series
observations.
True False 4. The smoothing constant is a number that determines how much weight it is attached to each
observation.
True False 5. Simple exponential smoothing is an appropriate method for prediction purposes when there
is a significant trend present in a time series data.
True False 6. The forecaster who uses MSD (mean squared deviations) to measure the effectiveness of
forecasting metho...
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 Winter '14
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