Question 1

What is a clear indicator of non-constant variance in a plot of regression model residuals?

Accepted Answer

A)  A non-linear trend in the residual plot. 
B)  An intercept standard error larger that the estimated intercept coefficient. 
C)  A funnel shaped trend in the residual plot. 
D)  The standard errors from each independent variable differ. 
A)  A non-linear trend in the residual plot. 
B)  An intercept standard error larger that the estimated intercept coefficient. 
C)  A funnel shaped trend in the residual plot. 
D)  The standard errors from each independent variable differ. C

Question 2

The regression residuals are computed as

Accepted Answer

A)    
B)    
C)    
D)    
A)    
B)    
C)    
D)    C

Question 3

Based on the following regression output, what is the equation of the regression line?

Accepted Answer

A)    
B)    
C)    
D)    
A)    
B)    
C)    
D)    A

Question 4

An analyst has identified 3 independent variables (X1, X2, X3) which might be used to predict Y. He has computed the regression equations using all combinations of the variables and the results are summarized in the following table. Why is the R2 value for the X3 model the same as the R2 value for the X1 and X3 model, but the Adjusted R2 values differ?

Accepted Answer

A)  The standard error for X1 is greater than the standard error for X3. 
B)  X1 does not reduce ESS enough to compensate for its addition to the model. 
C)  X1 does not reduce TSS enough to compensate for its addition to the model. 
D)  X1 and X3 represent similar factors so multicollinearity exists. 
A)  The standard error for X1 is greater than the standard error for X3. 
B)  X1 does not reduce ESS enough to compensate for its addition to the model. 
C)  X1 does not reduce TSS enough to compensate for its addition to the model. 
D)  X1 and X3 represent similar factors so multicollinearity exists.

Question 5

Estimation errors are often referred to as

Accepted Answer

A)  mistakes. 
B)  constant errors. 
C)  residuals. 
D)  squared errors. 
A)  mistakes. 
B)  constant errors. 
C)  residuals. 
D)  squared errors.

Question 6

Based on the following regression output, what conclusion can you reach about β0?

Accepted Answer

A)  β0 = 0, with P-value = 0.016353 
B)  β0 ≠ 0, with P-value = 0.016353 
C)  β0 = 0, with P-value = 0.000186 
D)  β0 ≠ 0, with P-value = 0.000186 
A)  β0 = 0, with P-value = 0.016353 
B)  β0 ≠ 0, with P-value = 0.016353 
C)  β0 = 0, with P-value = 0.000186 
D)  β0 ≠ 0, with P-value = 0.000186

Question 7

Exhibit 9.1
The following questions are based on the problem description and spreadsheet below.
A company has built a regression model to predict the number of labor hours (YS1U1B1iS1U1B0) required to process a batch of parts (XS1U1B1iS1U1B0). It has developed the following Excel spreadsheet of the results.  
-Refer to Exhibit 9.1. Interpret the meaning of the "Lower 95%" and "Upper 95%" terms in cells F16:G16 of the spreadsheet.

Accepted Answer

We are 95%

Question 8

​A simple linear regression model is of the form: Yi = $\beta$ +$\beta$1X1i + $\varepsilon$i

Accepted Answer

A) True 
 B)False

Question 9

You want to conduct a hypothesis test for β1. Based on the following regression output, what conclusion can you reach about β1?

Accepted Answer

A)  β1 = 0, with P-value = 0.016353 
B)  β1 ≠ 0, with P-value = 0.016353 
C)  β1 = 0, with P-value = 0.000186 
D)  β1 ≠ 0, with P-value = 0.000186 
A)  β1 = 0, with P-value = 0.016353 
B)  β1 ≠ 0, with P-value = 0.016353 
C)  β1 = 0, with P-value = 0.000186 
D)  β1 ≠ 0, with P-value = 0.000186

Question 10

Exhibit 9.7
The partial regression output below applies to the following questions.  
-Refer to Exhibit 9.7. What is the SS for Total?

Accepted Answer

The answer of Exhibit 9.7
The partial regression output below applies...

Question 11

​The R2 statistic (also referred to as the coefficient of determination) ranges in value from 0 to 1 (0 $\le$ R2 $\le$1) and indicates the proportion of the total variation in the dependent variable Y around its mean (average) that is accounted for by the independent variable(s) in the estimated regression function.

Accepted Answer

A) True 
 B)False

Question 12

Exhibit 9.1
The following questions are based on the problem description and spreadsheet below.
A company has built a regression model to predict the number of labor hours (YS1U1B1iS1U1B0) required to process a batch of parts (XS1U1B1iS1U1B0). It has developed the following Excel spreadsheet of the results.  
-Refer to Exhibit 9.1. Predict the mean number of labor hours for a batch of 5 parts.

Accepted Answer

The answer of Exhibit 9.1
The following questions are based on...

Question 13

Based on the following regression output, what proportion of the total variation in Y is explained by X?

Accepted Answer

A)  0.917214 
B)  0.841282 
C)  0.821442 
D)  9.385572 
A)  0.917214 
B)  0.841282 
C)  0.821442 
D)  9.385572

Question 14

Exhibit 9.2
The following questions are based on the problem description and spreadsheet below.
A paint manufacturer is interested in knowing how much pressure (in pounds per square inch, PSI) builds up inside aerosol cans at various temperatures (degrees Fahrenheit). It has developed the following Excel spreadsheet of the results.  
-Refer to Exhibit 9.2. Test the significance of the model and explain which values you used to reach your conclusions.

Accepted Answer

The regression model is signif

Question 15

The regression function indicates the

Accepted Answer

A)  average value the dependent variable assumes for a given value of the independent variable. 
B)  actual value the independent variable assumes for a given value of the dependent variable 
C)  average value the dependent variable assumes for a given value of the dependent variable 
D)  actual value the dependent variable assumes for a given value of the independent variable 
A)  average value the dependent variable assumes for a given value of the independent variable. 
B)  actual value the independent variable assumes for a given value of the dependent variable 
C)  average value the dependent variable assumes for a given value of the dependent variable 
D)  actual value the dependent variable assumes for a given value of the independent variable

Question 16

Exhibit 9.3
The following questions are based on the problem description and spreadsheet below.
A researcher is interested in determining how many calories young men consume. She measured the age of the individuals and recorded how much food they ate each day for a month. The average daily consumption was recorded as the dependent variable. She has developed the following Excel spreadsheet of the results.  
-Refer to Exhibit 9.3. Interpret the meaning of R square in cell B3 of the spreadsheet.

Accepted Answer

The R Square value of 0. 95871

Question 17

The standard error measures the

Accepted Answer

A)  variability in the X values. 
B)  variability in the actual data around the fitted regression function. 
C)  variability in the independent variable around the fitted regression function. 
D)  variability in the dependent variable around the fitted regression function. 
A)  variability in the X values. 
B)  variability in the actual data around the fitted regression function. 
C)  variability in the independent variable around the fitted regression function. 
D)  variability in the dependent variable around the fitted regression function.

Question 18

A residual is defined as the difference between the fitted value based on a model and a corresponding actual value.

Accepted Answer

A) True 
 B)False

Question 19

Exhibit 9.7
The partial regression output below applies to the following questions.  
-Refer to Exhibit 9.7. What is the SS for Residual and MS for Residual?

Accepted Answer

SS for Residual is 7

Question 20

In regression terms what does "best fit" mean?

Accepted Answer

A)  The estimated parameters, b0 and b1, are minimized. 
B)  The estimated parameters, b0 and b1, are linear. 
C)  The error terms are as small as possible. 
D)  The largest error term is as small as possible. 
A)  The estimated parameters, b0 and b1, are minimized. 
B)  The estimated parameters, b0 and b1, are linear. 
C)  The error terms are as small as possible. 
D)  The largest error term is as small as possible.

What is a clear indicator of non-constant variance in a plot of regression model residuals?

The regression residuals are computed as

Based on the following regression output, what is the equation of the regression line?

Estimation errors are often referred to as

Based on the following regression output, what conclusion can you reach about β₀?

A simple linear regression model is of the form: Y_i = $\beta$ + $\beta$ 1X₁_i + $\varepsilon$ i

You want to conduct a hypothesis test for β₁. Based on the following regression output, what conclusion can you reach about β₁?

Exhibit 9.7 The partial regression output below applies to the following questions. -Refer to Exhibit 9.7. What is the SS for Total?

Based on the following regression output, what proportion of the total variation in Y is explained by X?

The regression function indicates the

The standard error measures the

A residual is defined as the difference between the fitted value based on a model and a corresponding actual value.

Exhibit 9.7 The partial regression output below applies to the following questions. -Refer to Exhibit 9.7. What is the SS for Residual and MS for Residual?

In regression terms what does "best fit" mean?

Introduction to Modeling and Decision Analysis

Introduction to Optimization and Linear Programming

Modeling and Solving Lp Problems in a Spreadsheet

Sensitivity Analysis and the Simplex Method

Network Modeling

Integer Linear Programming

Goal Programming and Multiple Objective Optimization

Nonlinear Programming Evolutionary Optimization

Data Mining

Time Series Forecasting

Introduction to Simulation Using Analytic Solver Platform

Queuing Theory

Decision Analysis

Project Management Online

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Exam 9: Regression Analysis

What is a clear indicator of non-constant variance in a plot of regression model residuals?

The regression residuals are computed as

Based on the following regression output, what is the equation of the regression line?

Estimation errors are often referred to as

Based on the following regression output, what conclusion can you reach about β0?

​A simple linear regression model is of the form: Yi = β\betaβ + β\betaβ 1X1i + ε\varepsilonε i

You want to conduct a hypothesis test for β1. Based on the following regression output, what conclusion can you reach about β1?

Exhibit 9.7 The partial regression output below applies to the following questions. -Refer to Exhibit 9.7. What is the SS for Total?

Based on the following regression output, what proportion of the total variation in Y is explained by X?

The regression function indicates the

The standard error measures the

A residual is defined as the difference between the fitted value based on a model and a corresponding actual value.

Exhibit 9.7 The partial regression output below applies to the following questions. -Refer to Exhibit 9.7. What is the SS for Residual and MS for Residual?

In regression terms what does "best fit" mean?

Introduction to Modeling and Decision Analysis

Introduction to Optimization and Linear Programming

Modeling and Solving Lp Problems in a Spreadsheet

Sensitivity Analysis and the Simplex Method

Network Modeling

Integer Linear Programming

Goal Programming and Multiple Objective Optimization

Nonlinear Programming Evolutionary Optimization

Data Mining

Time Series Forecasting

Introduction to Simulation Using Analytic Solver Platform

Queuing Theory

Decision Analysis

Project Management Online

Filters

Based on the following regression output, what conclusion can you reach about β₀?

A simple linear regression model is of the form: Y_i = $\beta$ + $\beta$ 1X₁_i + $\varepsilon$ i

You want to conduct a hypothesis test for β₁. Based on the following regression output, what conclusion can you reach about β₁?