Question 1

Which of the following is an advantage of using the TREND() function versus the regression tool?

Accepted Answer

A)  The TREND() function provides more statistical information. 
B)  The TREND() function handles multiple dependent variable data. 
C)  The TREND() function is dynamically updated when input to the function changes. 
D)  The TREND() function does not use a least squares regression line. 
A)  The TREND() function provides more statistical information. 
B)  The TREND() function handles multiple dependent variable data. 
C)  The TREND() function is dynamically updated when input to the function changes. 
D)  The TREND() function does not use a least squares regression line.

Question 2

R2 is also referred to as

Accepted Answer

A)  coefficient of determination. 
B)  correlation coefficient. 
C)  total sum of squares. 
D)  regression sum of squares. 
A)  coefficient of determination. 
B)  correlation coefficient. 
C)  total sum of squares. 
D)  regression sum of squares.

Question 3

The company would like to build a prediction interval on the pressure for a can with a temperature of 125 degrees. What formula should be entered in cells B17:F21 of the following spreadsheet to compute this prediction interval? Partial results of the Regression analysis of the data are provided below.

Accepted Answer

The answer of The company would like to build a...

Question 4

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. Test the significance of the model and explain which values you used to reach your conclusions.

Accepted Answer

The regression coefficients ar

Question 5

Exhibit 9.5
The following questions are based on the description and spreadsheet below.
An analyst has identified 3 independent variables (XS1U1B11S1U1B0, XS1U1B12S1U1B0,XS1U1B13S1U1B0) which might be used to predict Y. He has computed the regression equations using all of the variables and the results are summarized in the following table.
 \(\begin{array}{lcccl}
\text { Independent }&&&&\
\text {Variable in}&&\text {Adjusted}&&\
\text { the Model } & R^{2} & -R^{2} & S_{0} & \text { Parameter Estimates }\ 
\hline \mathrm{X}_{1} & 0.00089 & -0.124 & 23.548 & \mathrm{~b}_{0}=93.7174, \mathrm{~b}_{1}=0.922 \
\mathrm{X}_{2} & 0.3870 & 0.3104 & 18.448 & \mathrm{~b}_{0}=57.0803, \mathrm{~b}_{2}=1.545 \
\mathrm{X}_{1} \text { and } \mathrm{X}_{2} & 0.3910 & 0.2170 & 19.654 & \mathrm{~b}_{0}=50.2927, \mathrm{~b}_{1}=1.952, \mathrm{~b}_{2}=1.554 \
\mathrm{X}_{3} & 0.8413 & 0.8214 & 9.3858 & \mathrm{~b}_{0}=31.6238, \mathrm{~b}_{3}=1.132 \
\mathrm{X}_{1} \text { and } \mathrm{X}_{3} & 0.8413 & 0.7960 & 10.033 & \mathrm{~b}_{0}=31.133, \mathrm{~b}_{1}=0.148, \mathrm{~b}_{3}=1.132 \
\mathrm{X}_{2} \text { and } \mathrm{X}_{3} & 0.9863 & 0.9824 & 2.948 & \mathrm{~b}_{0}=14.169, \mathrm{~b}_{2}=0.985, \mathrm{~b}_{3}=0.995 \
\mathrm{X}_{1} \text { X } \mathrm{X}_{2} \text { and } \mathrm{X}_{3} & 0.9871 & 0.9807 & 3.085 & \mathrm{~b}_{0}=11.113 . \mathrm{b}_{1}=0.899 . \mathrm{b}_{2}=0.990 . \mathrm{b}_{3}=0.993
\end{array}\)

-Refer to Exhibit 9.5. Based on the data in the table which is the best model for the charity to use? Explain which values you used to reach your conclusion.

Accepted Answer

The model with X₂ and

Question 6

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? \(\begin{array}{lcccl}
\text { Independent}\
\text { Variable in the}&\text { Adjusted}\ 
\hline\text { Model } & \mathrm{R}^{2} & -\mathrm{R}^{2} & \mathrm{~S}_{\mathrm{e}} & \text { Parameter Estimates } \
\hline \mathrm{X}_{1} & 0.00089 & -0.124 & 23.548 & \mathrm{~b}_{0}=93.7174, \mathrm{~b}_{1}=0.922 \
\mathrm{X}_{2} & 0.3870 & 0.3104 & 18.448 & \mathrm{~b}_{0}=57.0803, \mathrm{~b}_{2}=1.545 \
\mathrm{X}_{1} \text { and } \mathrm{X}_{2} & 0.3910 & 0.2170 & 19.654 & \mathrm{~b}_{0}=50.2927, \mathrm{~b}_{1}=1.952, \mathrm{~b}_{2}=1.554 \
\mathrm{X}_{3} & 0.8413 & 0.8214 & 9.3858 & \mathrm{~b}_{0}=31.6238, \mathrm{~b}_{3}=1.132 \
\mathrm{X}_{1} \text { and } \mathrm{X}_{3} & 0.8413 & 0.7960 & 10.033 & \mathrm{~b}_{0}=31.133, \mathrm{~b}_{1}=0.148, \mathrm{~b}_{3}=1.132 \
\mathrm{X}_{2} \text { and } \mathrm{X}_{3} & 0.9863 & 0.9824 & 2.948 & \mathrm{~b}_{0}=14.169, \mathrm{~b}_{2}=0.985, \mathrm{~b}_{3}=0.995 \
\text { All three } & 0.9871 & 0.9807 & 3.085 & \mathrm{~b}_{0}=11.113, \mathrm{~b}_{1}=0.899, \mathrm{~b}_{2}=0.990 \
&&&&\mathrm{~b}_{3}=0.993
\end{array}\)

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 7

The actual value of a dependent variable will generally differ from the regression equation estimate due to

Accepted Answer

A)  unaccounted for random variation. 
B)  the inability of the nonlinear Solver to find optimal values. 
C)  not building the regression model with enough data. 
D)  the model R2 not equal to 1. 
A)  unaccounted for random variation. 
B)  the inability of the nonlinear Solver to find optimal values. 
C)  not building the regression model with enough data. 
D)  the model R2 not equal to 1.

Question 8

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. Interpret the meaning of R Square in cell B3 of the spreadsheet.

Accepted Answer

The R Square value of 0.9998 m

Question 9

When using the Regression tool in Excel the independent variable is entered as the

Accepted Answer

A)  X-range. 
B)  Y-range. 
C)  dependent-range. 
D)  independent-range. 
A)  X-range. 
B)  Y-range. 
C)  dependent-range. 
D)  independent-range.

Question 10

The company would like to build a prediction interval on the time for a new batch of 8 parts. What formula should be entered in cells B17:F21 of the following spreadsheet to compute this prediction interval? Partial results of the Regression analysis of the data are provided below.

Accepted Answer

The answer of The company would like to build a...

Question 11

Based on the following regression output, what is the equation of the regression line? $\begin{array}{|l|r|l|l|l|l|}
\hline {\text { Regression Statistics }} & & & & \
\hline \text { Multiple R } & 0.917214 & & & & \
\hline \text { R Square } & 0.841282 & & & & \
\hline \text { Adjusted R Square } & 0.821442 & & & & \
\hline \text { Standard Error } & 9.385572 & & & & \
\hline \text { Observations } & 10 & & & & \
\hline & & & & & \
\hline\text { ANOVA } & & & & & \ 
\hline &\text { df } & \text { SS } & \text { MS } & F & \text { Significance F } \
\hline \text { Regression } & 1 & 3735.306 & 3735.306 & 42.40379 & 0.000186 \
\hline \text { Residual } & 8 & 704.7117 & 88.08896 & & \
\hline \text { Total } & 9 & 4440.017 & & & \
\hline & & & & & \
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } \
\hline \text { Intercept } & 31.62378 & 10.44297 & 3.028236 & 0.016353 & 7.542233 \
\hline \text { X Variable 1 } & 1.131661 & 0.173786 & 6.511819 & 0.000186 & 0.73091\
\hline
\end{array}$

Accepted Answer

A)  i = 1.131661 + 31.62378 X1i 
B)  i = 31.62378 + 1.131661 X1i 
C)  i = 3.028236 + 6.511819 X1i 
D)  i = 7.542233 + 0.73091 X1i 
A)  i = 1.131661 + 31.62378 X1i 
B)  i = 31.62378 + 1.131661 X1i 
C)  i = 3.028236 + 6.511819 X1i 
D)  i = 7.542233 + 0.73091 X1i

Question 12

Exhibit 9.6
The partial regression output below applies to the following questions.
  
-Refer to Exhibit 9.6. What is the F-statistic value?

Accepted Answer

The answer of Exhibit 9.6
The partial regression output below applies...

Question 13

The estimated value of Y1 is given by

Accepted Answer

A) $$\hat { Y } _ { 1 } = b _ { 0 } + b _ { 1 } X _ { 1 }$$ 
B) $$\hat { Y } _ { 1 } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 }$$ 
C) $$\hat { Y } _ { 1 } = b _ { 0 } + b _ { 1 } X _ { 1 } + \varepsilon$$ 
D) $$\tilde { Y } _ { 1 } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \varepsilon$$ 
A) $$\hat { Y } _ { 1 } = b _ { 0 } + b _ { 1 } X _ { 1 }$$ 
B) $$\hat { Y } _ { 1 } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 }$$ 
C) $$\hat { Y } _ { 1 } = b _ { 0 } + b _ { 1 } X _ { 1 } + \varepsilon$$ 
D) $$\tilde { Y } _ { 1 } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \varepsilon$$

Question 14

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 R Square in cell B3 of the spreadsheet.

Accepted Answer

The R Square value of 0.9941 m

Question 15

The method of least squares finds parameter values that

Accepted Answer

A)  minimizes TSS. 
B)  minimizes RSS. 
C)  minimizes ESS. 
D)  minimizes ESS + RSS. 
A)  minimizes TSS. 
B)  minimizes RSS. 
C)  minimizes ESS. 
D)  minimizes ESS + RSS.

Question 16

The researcher would like to build a prediction interval on the calories consumed by an 18 year old man. What formula should be entered in cells B17:F21 of the following spreadsheet to compute this prediction interval? Partial results of the Regression analysis of the data are provided below.

Accepted Answer

The answer of The researcher would like to build a...

Question 17

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% confident

Question 18

How many binary variables are required to encode a persons age group as being either young, middle-age or old? What are the variables and what are the meanings of their 0, 1 values?

Accepted Answer

Two binary variables are neede

Question 19

Based on the following regression output, what proportion of the total variation in Y is explained by X? $\begin{array}{|l|r|l|l|l|l|}
\hline {\text { Regression Statistics }} & & & & \
\hline \text { Multiple R } & 0.917214 & & & & \
\hline \text { R Square } & 0.841282 & & & & \
\hline \text { Adjusted R Square } & 0.821442 & & & & \
\hline \text { Standard Error } & 9.385572 & & & & \
\hline \text { Observations } & 10 & & & & \
\hline & & & & & \
\hline\text { ANOVA } & & & & & \ 
\hline &\text { df } & \text { SS } & \text { MS } & F & \text { Significance F } \
\hline \text { Regression } & 1 & 3735.306 & 3735.306 & 42.40379 & 0.000186 \
\hline \text { Residual } & 8 & 704.7117 & 88.08896 & & \
\hline \text { Total } & 9 & 4440.017 & & & \
\hline & & & & & \
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } \
\hline \text { Intercept } & 31.62378 & 10.44297 & 3.028236 & 0.016353 & 7.542233 \
\hline \text { X Variable 1 } & 1.131661 & 0.173786 & 6.511819 & 0.000186 & 0.73091\
\hline
\end{array}$

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 20

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. Predict the mean pressure for a temperature of 120 degrees.

Accepted Answer

_i = 38.1923

Which of the following is an advantage of using the TREND() function versus the regression tool?

R² is also referred to as

The actual value of a dependent variable will generally differ from the regression equation estimate due to

When using the Regression tool in Excel the independent variable is entered as the

The company would like to build a prediction interval on the time for a new batch of 8 parts. What formula should be entered in cells B17:F21 of the following spreadsheet to compute this prediction interval? Partial results of the Regression analysis of the data are provided below.

Exhibit 9.6 The partial regression output below applies to the following questions. -Refer to Exhibit 9.6. What is the F-statistic value?

The estimated value of Y₁ is given by

The method of least squares finds parameter values that

The researcher would like to build a prediction interval on the calories consumed by an 18 year old man. What formula should be entered in cells B17:F21 of the following spreadsheet to compute this prediction interval? Partial results of the Regression analysis of the data are provided below.

How many binary variables are required to encode a persons age group as being either young, middle-age or old? What are the variables and what are the meanings of their 0, 1 values?

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 and Evolutionary Optimization

Discriminant Analysis

Time Series Forecasting

Introduction to Simulation Using Risk Solver Platform

Queuing Theory

Decision Analysis

Project Management Online

Filters

Exam 9: Regression Analysis

Which of the following is an advantage of using the TREND() function versus the regression tool?

R2 is also referred to as

The actual value of a dependent variable will generally differ from the regression equation estimate due to

When using the Regression tool in Excel the independent variable is entered as the

The company would like to build a prediction interval on the time for a new batch of 8 parts. What formula should be entered in cells B17:F21 of the following spreadsheet to compute this prediction interval? Partial results of the Regression analysis of the data are provided below.

Exhibit 9.6 The partial regression output below applies to the following questions. -Refer to Exhibit 9.6. What is the F-statistic value?

The estimated value of Y1 is given by

The method of least squares finds parameter values that

The researcher would like to build a prediction interval on the calories consumed by an 18 year old man. What formula should be entered in cells B17:F21 of the following spreadsheet to compute this prediction interval? Partial results of the Regression analysis of the data are provided below.

How many binary variables are required to encode a persons age group as being either young, middle-age or old? What are the variables and what are the meanings of their 0, 1 values?

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 and Evolutionary Optimization

Discriminant Analysis

Time Series Forecasting

Introduction to Simulation Using Risk Solver Platform

Queuing Theory

Decision Analysis

Project Management Online

Filters

R² is also referred to as

The estimated value of Y₁ is given by