Question 1

The regression line denotes the ____ between the dependent and independent variables.

Accepted Answer

A)  unsystematic variation 
B)  systematic variation 
C)  random variation 
D)  average variation 
A)  unsystematic variation 
B)  systematic variation 
C)  random variation 
D)  average variation

Question 2

The term F1F1F1S1F1F1F10 in the regression model represents

Accepted Answer

A)  the slope of the regression model. 
B)  a random error term. 
C)  a correction for mistakes in measuring X. 
D)  a correction for the fact that we are taking a sample. 
A)  the slope of the regression model. 
B)  a random error term. 
C)  a correction for mistakes in measuring X. 
D)  a correction for the fact that we are taking a sample.

Question 3

The error term F1F1F1S1F1F1F10 in a regression model represents

Accepted Answer

A)  a random error in the data. 
B)  unsystematic variation in the dependent variable. 
C)  variation not explained by the independent variables. 
D)  all of these. 
A)  a random error in the data. 
B)  unsystematic variation in the dependent variable. 
C)  variation not explained by the independent variables. 
D)  all of these.

Question 4

The error sum of squares term is used as a criterion for determining b0 and b1 because

Accepted Answer

A)  the sum of errors will always equal zero. 
B)  the term can be solved for exact values of b0 and b1. 
C)  both b0 and b1 can be easily calculated using the sum of squares term. 
D)  all of these. 
A)  the sum of errors will always equal zero. 
B)  the term can be solved for exact values of b0 and b1. 
C)  both b0 and b1 can be easily calculated using the sum of squares term. 
D)  all of these.

Question 5

Regression analysis is a modeling technique

Accepted Answer

A)  that assumes all data is normally distributed. 
B)  for analyzing the relationship between dependent and independent variables. 
C)  for examining linear trend data only. 
D)  for capturing uncertainty in predicted values of Y. 
A)  that assumes all data is normally distributed. 
B)  for analyzing the relationship between dependent and independent variables. 
C)  for examining linear trend data only. 
D)  for capturing uncertainty in predicted values of Y.

Question 6

What goodness-of-fit measure is commonly used to evaluate a multiple regression function?

Accepted Answer

A)  R2 
B)  adjusted R2 
C)  partial R2 
D)  total R2 
A)  R2 
B)  adjusted R2 
C)  partial R2 
D)  total R2

Question 7

What is the correct range for R2 values?

Accepted Answer

A)  (F1F1F1S1F1F1F101 F1F1F1S1F1F1F10 R2 F1F1F1S1F1F1F10 0) 
B)  (F1F1F1S1F1F1F101 F1F1F1S1F1F1F10 R2 F1F1F1S1F1F1F10 1) 
C)  (0 F1F1F1S1F1F1F10 R2 F1F1F1S1F1F1F10 1) 
D)  (0 F1F1F1S1F1F1F10 R2 F1F1F1S1F1F1F10 .5) 
A)  (F1F1F1S1F1F1F101 F1F1F1S1F1F1F10 R2 F1F1F1S1F1F1F10 0) 
B)  (F1F1F1S1F1F1F101 F1F1F1S1F1F1F10 R2 F1F1F1S1F1F1F10 1) 
C)  (0 F1F1F1S1F1F1F10 R2 F1F1F1S1F1F1F10 1) 
D)  (0 F1F1F1S1F1F1F10 R2 F1F1F1S1F1F1F10 .5)

Question 8

The problem of finding the optimal values of b0 and b1 is

Accepted Answer

A)  a linear programming problem. 
B)  an unconstrained nonlinear optimization problem. 
C)  a goal programming problem. 
D)  a constrained nonlinear optimization problem. 
A)  a linear programming problem. 
B)  an unconstrained nonlinear optimization problem. 
C)  a goal programming problem. 
D)  a constrained nonlinear optimization problem.

Question 9

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. What is the estimated regression function for this problem? Explain what the terms in your equation mean.

Accepted Answer

_i = 38.1923 + 1.2447 X_1i. The ave

Question 10

Exhibit 9.6
The partial regression output below applies to the following questions.
  
-Refer to Exhibit 9.6. What is the MS for Residual?

Accepted Answer

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

Question 11

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 12

Based on the following regression output, what conclusion can you reach about F1F1F1S1?F1F1F100? $\begin{array}{|l|r|l|l|l|l|}
\hline {\text { Regression Statistics }} & & & & \
\hline \text { Multiple R } & 0.917214 & & & & \
\hline \text { R Square } & 0.841282 & & & & \
\hline \text { Adjusted R Square } & 0.821442 & & & & \
\hline \text { Standard Error } & 9.385572 & & & & \
\hline \text { Observations } & 10 & & & & \
\hline & & & & & \
\hline\text { ANOVA } & & & & & \ 
\hline &\text { df } & \text { SS } & \text { MS } & F & \text { Significance F } \
\hline \text { Regression } & 1 & 3735.306 & 3735.306 & 42.40379 & 0.000186 \
\hline \text { Residual } & 8 & 704.7117 & 88.08896 & & \
\hline \text { Total } & 9 & 4440.017 & & & \
\hline & & & & & \
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } \
\hline \text { Intercept } & 31.62378 & 10.44297 & 3.028236 & 0.016353 & 7.542233 \
\hline \text { X Variable 1 } & 1.131661 & 0.173786 & 6.511819 & 0.000186 & 0.73091\
\hline
\end{array}$

Accepted Answer

A)  F1F1F1S1?F1F1F100 = 0, with P-value = 0.016353 
B)  F1F1F1S1?F1F1F100 F1F1F1S1?F1F1F10 0, with P-value = 0.016353 
C)  F1F1F1S1?F1F1F100 = 0, with P-value = 0.000186 
D)  F1F1F1S1?F1F1F100 F1F1F1S1?F1F1F10 0, with P-value = 0.000186 
A)  F1F1F1S1?F1F1F100 = 0, with P-value = 0.016353 
B)  F1F1F1S1?F1F1F100 F1F1F1S1?F1F1F10 0, with P-value = 0.016353 
C)  F1F1F1S1?F1F1F100 = 0, with P-value = 0.000186 
D)  F1F1F1S1?F1F1F100 F1F1F1S1?F1F1F10 0, with P-value = 0.000186

Question 13

Based on the following regression output, what is the equation of the regression line? \(\begin{array}{|l|r|r|r|r|r|}
\hline \text { Regression Statistics } & & & & & \
\hline \text { Multiple R } & 0.99313 & & & & \
\hline \text { R Square } & 0.98630 & & & & \
\hline \begin{array}{l}
\text { Adjusted R } \
\text { Square }
\end{array} & 0.98238 & & & & \
\hline \text { Standard Error } & 2.94802 & & & & \
\hline \text { Observations } & 10 & & & & \
\hline & & & & & \
\hline \text { ANOVA } & & & & & \
\hline &{\text { df }} & {\text { SS }} & {\text { MS }} &{F} & \text { Significance F } \
\hline \text { Regression } & 2 & 4379.182 & 2189.591 & 251.943 & 0.0000 \
\hline \text { Residual } & 7 & 60.836 & 8.691 & & \
\hline \text { Total } & 9 & 4440.017 & & & \
\hline & & & & & \
\hline & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & \text { P-value } & \text { Lower 95\% } \
\hline \text { Intercept } & 14.169 & 3.856 & 3.674 & 0.008 & 5.050 \
\hline \text { X Variable 1 } & 0.985 & 0.114 & 8.607 & 0.000 & 0.714 \
\hline \text { X Variable 2 } & 0.995 & 0.057 & 17.498 & 0.000 & 0.860\
\hline 
\end{array}\)

Accepted Answer

A)  i = 14.169 + 0.985 X1i + 0.995 X2i 
B)  i = 14.169 + 0.995 X1i + 0.985 X2i 
C)  i = 0.995 + 14.169 X1i + 0.985 X2i 
D)  i = 3.856 + 0.114 X1i + 0.057 X2i 
A)  i = 14.169 + 0.985 X1i + 0.995 X2i 
B)  i = 14.169 + 0.995 X1i + 0.985 X2i 
C)  i = 0.995 + 14.169 X1i + 0.985 X2i 
D)  i = 3.856 + 0.114 X1i + 0.057 X2i

Question 14

Exhibit 9.4
The following questions are based on the problem description and spreadsheet below.
A charitable organization wants to determine what type of people donate to charities like itself. The charity felt that a person's education (in years), annual income, ($1,000) and the number of children the person had were important variables to consider. The charity developed regression models for all of the possible combinations of these three variables but does not know what to do with the results.
  
-Refer to Exhibit 9.4. 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

Education and Kids h

Question 15

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. Predict the mean number of calories consumed by a 19 year old man.

Accepted Answer

_i = 3995.991 F1F1F1S1

Question 16

The R2 statistic

Accepted Answer

A)  varies between F1F1F1S1F1F1F101 and 1. 
B)  compares the regression sum of squares to the total sum of squares. 
C)  accounts for the number of parameters in the regression model. 
D)  is the ratio of the error sum of squares to the regression sum of squares. 
A)  varies between F1F1F1S1F1F1F101 and 1. 
B)  compares the regression sum of squares to the total sum of squares. 
C)  accounts for the number of parameters in the regression model. 
D)  is the ratio of the error sum of squares to the regression sum of squares.

The regression line denotes the ____ between the dependent and independent variables.

The term  in the regression model represents

The error term  in a regression model represents

The error sum of squares term is used as a criterion for determining b₀ and b₁ because

Regression analysis is a modeling technique

What goodness-of-fit measure is commonly used to evaluate a multiple regression function?

What is the correct range for R²values?

The problem of finding the optimal values of b₀ and b₁ is

Exhibit 9.6 The partial regression output below applies to the following questions. -Refer to Exhibit 9.6. What is the MS for Residual?

Estimation errors are often referred to as

The R² statistic

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

The regression line denotes the ____ between the dependent and independent variables.

The term  in the regression model represents

The error term  in a regression model represents

The error sum of squares term is used as a criterion for determining b0 and b1 because

Regression analysis is a modeling technique

What goodness-of-fit measure is commonly used to evaluate a multiple regression function?

What is the correct range for R2 values?

The problem of finding the optimal values of b0 and b1 is

Exhibit 9.6 The partial regression output below applies to the following questions. -Refer to Exhibit 9.6. What is the MS for Residual?

Estimation errors are often referred to as

The R2 statistic

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

The error sum of squares term is used as a criterion for determining b₀ and b₁ because

What is the correct range for R²values?

The problem of finding the optimal values of b₀ and b₁ is

The R² statistic