Question 1

In multiple regression, R Square is referred to as the:

Accepted Answer

A)  multiple correlation coefficient. 
B)  coefficient of autocorrelation. 
C)  coefficient of multiple determination. 
D)  multiple significance coefficient. 
A)  multiple correlation coefficient. 
B)  coefficient of autocorrelation. 
C)  coefficient of multiple determination. 
D)  multiple significance coefficient. C

Question 2

The best-fitting line maximizes the residuals.

Accepted Answer

A) True 
 B)False  False

Question 3

Identify the components of simple linear regression models and discuss their applications
The following table exhibits the age of antique furniture and the corresponding prices. Use the table to answer the following question(s). (Hint: Use scatter diagram and the Excel Trendline tool where necessary). $$\begin{array} { | l | l | } 
\hline \begin{array} { l } 
\text { Number } \
\text { of years }
\end{array} & \begin{array} { l } 
\text { Value } \
\text { (\$) }
\end{array} \
\hline 78 & 930 \
\hline 91 & 1010 \
\hline 83 & 970 \
\hline 159 & 1950 \
\hline 134 & 1610 \
\hline 210 & 2880 \
\hline 88 & 980 \
\hline 178 & 2010 \
\hline 124 & 1370 \
\hline 72 & 900 \
\hline
\end{array}$$
-What is the relationship between the age of the furniture and their values?

Accepted Answer

A)  Nonlinear 
B)  Linear 
C)  Curvilinear 
D)  No relationship 
A)  Nonlinear 
B)  Linear 
C)  Curvilinear 
D)  No relationship B

Question 4

Identify the components of simple linear regression models and discuss their applications Use the data given below to answer the following question(s).
Following is an extract from the database of a construction company. The table shows the height of walls in feet and the cost of raising them. The estimated simple linear regression equation is given as Ŷ = b0 + b1X. (Hint: Use Excel functions). $$\begin{array} { | l | l | } 
\hline\begin{array} { l } 
\text { Height } \
\text { (ft) }
\end{array} & \text { Cost (\$) } \
\hline 4 & 670 \
\hline 3 & 430 \
\hline 7 & 810 \
\hline 9 & 1100 \
\hline 6 & 790 \
\hline 8 & 880 \
\hline 5 & 760 \
\hline 11 & 1200 \
\hline
\end{array}$$
-What is the estimated cost of raising a 10-inch wall?

Accepted Answer

A)  1505.786 
B)  1103.578 
C)  968.6109 
D)  1123.008 
A)  1505.786 
B)  1103.578 
C)  968.6109 
D)  1123.008

Question 5

A regression model that involves a single independent variable is called .

Accepted Answer

A)  single regression 
B)  unit regression 
C)  simple regression 
D)  individual regression 
A)  single regression 
B)  unit regression 
C)  simple regression 
D)  individual regression

Question 6

When a scatter chart of data shows a nonlinear relationship, the nonlinear model can be expressed as:

Accepted Answer

A)  Y = β0 + β1X + β2X2 + ε 
B)  Y = β0 + β1X + (β2X)2 + ε 
C)  Y = β0 + β1X + β2X 
D)  Y = β0 + β1X2 + β2X2 + ε 
A)  Y = β0 + β1X + β2X2 + ε 
B)  Y = β0 + β1X + (β2X)2 + ε 
C)  Y = β0 + β1X + β2X 
D)  Y = β0 + β1X2 + β2X2 + ε

Question 7

Creating a scatter chart with an added trendline is visually superior to the scatter chart generated by line fit plots.

Accepted Answer

A) True 
 B)False

Question 8

Which of the following is true about multiple linear regression?

Accepted Answer

A)  It is a linear regression model with more than one dependent variable. 
B)  The regression coefficients are called fractional regression coefficients. 
C)  It uses least squares to estimate the intercept and slope coefficients. 
D)  The ANOVA tests for the significance of each variable separately. 
A)  It is a linear regression model with more than one dependent variable. 
B)  The regression coefficients are called fractional regression coefficients. 
C)  It uses least squares to estimate the intercept and slope coefficients. 
D)  The ANOVA tests for the significance of each variable separately.

Question 9

In functions, represented by y = abx, y rises or falls at constantly increasing rates.

Accepted Answer

A)  logarithmic 
B)  power 
C)  exponential 
D)  polynomial 
A)  logarithmic 
B)  power 
C)  exponential 
D)  polynomial

Question 10

Identify the components of simple linear regression models and discuss their applications Use the data given below to answer the following question(s).
Following is an extract from the database of a construction company. The table shows the height of walls in feet and the cost of raising them. The estimated simple linear regression equation is given as Ŷ = b0 + b1X. (Hint: Use Excel functions). $$\begin{array} { | l | l | } 
\hline\begin{array} { l } 
\text { Height } \
\text { (ft) }
\end{array} & \text { Cost (\$) } \
\hline 4 & 670 \
\hline 3 & 430 \
\hline 7 & 810 \
\hline 9 & 1100 \
\hline 6 & 790 \
\hline 8 & 880 \
\hline 5 & 760 \
\hline 11 & 1200 \
\hline
\end{array}$$
-What is the value of the coefficient b0?

Accepted Answer

A)  -2.25321 
B)  0.010697 
C)  254.8371 
D)  86.81704 
A)  -2.25321 
B)  0.010697 
C)  254.8371 
D)  86.81704

Question 11

While conducting regression analysis, how is constructing a normal probability plot useful?

Accepted Answer

While conducting regressions analysis us

Question 12

The standard error may be assumed to be large if the data are clustered close to the regression line.

Accepted Answer

A) True 
 B)False

Question 13

When two or more independent variables in the same regression model can predict each other better than the dependent variable, the condition is referred to as .

Accepted Answer

A)  autocorrelation 
B)  heteroscedasticity 
C)  multicollinearity 
D)  homoscedasticity 
A)  autocorrelation 
B)  heteroscedasticity 
C)  multicollinearity 
D)  homoscedasticity

Question 14

Identify the components of simple linear regression models and discuss their applications
The following table exhibits the age of antique furniture and the corresponding prices. Use the table to answer the following question(s). (Hint: Use scatter diagram and the Excel Trendline tool where necessary). $$\begin{array} { | l | l | } 
\hline \begin{array} { l } 
\text { Number } \
\text { of years }
\end{array} & \begin{array} { l } 
\text { Value } \
\text { (\$) }
\end{array} \
\hline 78 & 930 \
\hline 91 & 1010 \
\hline 83 & 970 \
\hline 159 & 1950 \
\hline 134 & 1610 \
\hline 210 & 2880 \
\hline 88 & 980 \
\hline 178 & 2010 \
\hline 124 & 1370 \
\hline 72 & 900 \
\hline
\end{array}$$
-Which of the following is true of linear functions used in predictive analytical models?

Accepted Answer

A)  It is used when the rate of change in a variable decreases or increases quickly and then levels out. 
B)  It is used when there is a steady decrease or increase over a range of a variable. 
C)  It is used when there is increase at a specific rate. 
D)  It is used when there is a rise or fall at a constantly increasing rate. 
A)  It is used when the rate of change in a variable decreases or increases quickly and then levels out. 
B)  It is used when there is a steady decrease or increase over a range of a variable. 
C)  It is used when there is increase at a specific rate. 
D)  It is used when there is a rise or fall at a constantly increasing rate.

Question 15

While testing hypotheses for regression coefficients, the t-test for the slope is expressed as:

Accepted Answer

A)  $ t=\frac{b_{1}-0}{\text { standard error }} $ 
B)  $ t=\frac{b_{1}-0}{b_{0}+b_{1}} $ 
C)  $ t=\frac{b_{1}}{b_{0}-b_{1}} $ 
D)  $ t=\frac{(\text { standard error })^{2}}{b_{0}-b_{1}} $ 
A)  $ t=\frac{b_{1}-0}{\text { standard error }} $ 
B)  $ t=\frac{b_{1}-0}{b_{0}+b_{1}} $ 
C)  $ t=\frac{b_{1}}{b_{0}-b_{1}} $ 
D)  $ t=\frac{(\text { standard error })^{2}}{b_{0}-b_{1}} $

Question 16

Use the data given below to answer the following question(s).
Following is an extract from a firm's database detailing the number of hours spent on the job by employees and their corresponding pay. (Note: Assume a level of significance of 0.05 wherever necessary.) $$\begin{array} { | l | l | } 
\hline \begin{array} { l } 
\text { Hours spent } \
\text { on the job }
\end{array} & \text { Salary (\$) } \
\hline 4 & 340 \
\hline 12 & 850 \
\hline 7 & 570 \
\hline 5 & 470 \
\hline 11 & 820 \
\hline 8 & 610 \
\hline 9 & 630 \
\hline 13 & 900 \
\hline 10 & 800 \
\hline 6 & 480 \
\hline
\end{array}$$
-Construct a scatter diagram and use the Excel Trendline tool to find the best-fitting simple linear regression model.

Accepted Answer

The answer of Use the data given below to answer...

Question 17

Interpret the confidence intervals.

Accepted Answer

A 95% confidence interval for the interc

Question 18

Interpret residual output.

Accepted Answer

From the data, the first observation has

Question 19

The R2 value:

Accepted Answer

A)  is the variability of the observed Y-values from the predicted values. 
B)  indicates that as the independent variable increases, the intercept term does too. 
C)  gives the proportion of variation in the dependent variable that is explained by the independent variable. 
D)  transforms the cumulative probability scale (vertical axis) so that the graph of the cumulative normal distribution is a straight line. 
A)  is the variability of the observed Y-values from the predicted values. 
B)  indicates that as the independent variable increases, the intercept term does too. 
C)  gives the proportion of variation in the dependent variable that is explained by the independent variable. 
D)  transforms the cumulative probability scale (vertical axis) so that the graph of the cumulative normal distribution is a straight line.

Question 20

A(n) is an extreme value that is different from the rest of the data.

Accepted Answer

A)  critical value 
B)  standard error 
C)  expected value 
D)  outlier 
A)  critical value 
B)  standard error 
C)  expected value 
D)  outlier

In multiple regression, R Square is referred to as the:

The best-fitting line maximizes the residuals.

A regression model that involves a single independent variable is called .

When a scatter chart of data shows a nonlinear relationship, the nonlinear model can be expressed as:

Creating a scatter chart with an added trendline is visually superior to the scatter chart generated by line fit plots.

Which of the following is true about multiple linear regression?

In functions, represented by y = abx, y rises or falls at constantly increasing rates.

While conducting regression analysis, how is constructing a normal probability plot useful?

The standard error may be assumed to be large if the data are clustered close to the regression line.

When two or more independent variables in the same regression model can predict each other better than the dependent variable, the condition is referred to as .

While testing hypotheses for regression coefficients, the t-test for the slope is expressed as:

Interpret the confidence intervals.

Interpret residual output.

The R² value:

A(n) is an extreme value that is different from the rest of the data.

Business Analytics

Analytics on Spreadsheets

Visualizing and Exploring Data

Descriptive Statistical Measures

Probability Distributions and Data Modeling

Sampling and Estimation

Statistical Inference

Forecasting Techniques

Introduction to Data Mining

Spreadsheet Modeling and Analysis

Monte Carlo Simulation and Risk Analysis

Linear Optimization

Applications of Linear Optimization

Integer Optimization

Decision Analysis

Filters

Exam 8: Trendlines and Regression Analysis

In multiple regression, R Square is referred to as the:

The best-fitting line maximizes the residuals.

A regression model that involves a single independent variable is called .

When a scatter chart of data shows a nonlinear relationship, the nonlinear model can be expressed as:

Creating a scatter chart with an added trendline is visually superior to the scatter chart generated by line fit plots.

Which of the following is true about multiple linear regression?

In functions, represented by y = abx, y rises or falls at constantly increasing rates.

While conducting regression analysis, how is constructing a normal probability plot useful?

The standard error may be assumed to be large if the data are clustered close to the regression line.

When two or more independent variables in the same regression model can predict each other better than the dependent variable, the condition is referred to as .

While testing hypotheses for regression coefficients, the t-test for the slope is expressed as:

Interpret the confidence intervals.

Interpret residual output.

The R2 value:

A(n) is an extreme value that is different from the rest of the data.

Business Analytics

Analytics on Spreadsheets

Visualizing and Exploring Data

Descriptive Statistical Measures

Probability Distributions and Data Modeling

Sampling and Estimation

Statistical Inference

Forecasting Techniques

Introduction to Data Mining

Spreadsheet Modeling and Analysis

Monte Carlo Simulation and Risk Analysis

Linear Optimization

Applications of Linear Optimization

Integer Optimization

Decision Analysis

Filters

The R² value: