Question 1

In a linear relationship, which of the following accounts for the many possible values of the dependent variable that vary around the mean?

Accepted Answer

A)  the coefficient of the dependent variable X 
B)  the value of the intercept ß0 
C)  the random error term ε 
D)  the standard error SYX 
A)  the coefficient of the dependent variable X 
B)  the value of the intercept ß0 
C)  the random error term ε 
D)  the standard error SYX

Question 2

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). $$\begin{array} { | l | l | } 
\hline\begin{array} { l } 
\text { Height } \
\text { (ft) }
\end{array} & \text { Cost (\$) } \
\hline 4 & 670 \
\hline 3 & 430 \
\hline 7 & 810 \
\hline 9 & 1100 \
\hline 6 & 790 \
\hline 8 & 880 \
\hline 5 & 760 \
\hline 11 & 1200 \
\hline
\end{array}$$
-Which of the following statements is true when using the Excel Regression tool?

Accepted Answer

A)  The range for the independent variable values must be specified in the box for the Input Y Range. 
B)  Checking the option Constant is Zero forces the intercept to zero. 
C)  The Regression tool can be found in the Tools tab under Insert group. 
D)  Adding an intercept term reduces the analysis' fit to the data. 
A)  The range for the independent variable values must be specified in the box for the Input Y Range. 
B)  Checking the option Constant is Zero forces the intercept to zero. 
C)  The Regression tool can be found in the Tools tab under Insert group. 
D)  Adding an intercept term reduces the analysis' fit to the data.

Question 3

What is the expected value for a 90 year-old piece of furniture?

Accepted Answer

A)  $1002.45 
B)  $997.98 
C)  $934.56 
D)  $1033.21 
A)  $1002.45 
B)  $997.98 
C)  $934.56 
D)  $1033.21

Question 4

Which of the following is true about the observed errors associated with estimating the value of the dependent variable using the regression line?

Accepted Answer

A)  They are the horizontal distances between slopes and y-intercepts. 
B)  The errors are also referred to as critical values. 
C)  They are always maximized by the regression lines. 
D)  The errors can be negative or positive. 
A)  They are the horizontal distances between slopes and y-intercepts. 
B)  The errors are also referred to as critical values. 
C)  They are always maximized by the regression lines. 
D)  The errors can be negative or positive.

Question 5

Which of the following is true when testing for normality of errors?

Accepted Answer

A)  Normality is verified by inspecting for a bell-shaped distribution. 
B)  It is easier to evaluate normality with small sample sizes. 
C)  A scatter diagram of the whole data is always used to verify normality. 
D)  Errors are normally distributed when the scatter diagram shows a straight-line distribution. 
A)  Normality is verified by inspecting for a bell-shaped distribution. 
B)  It is easier to evaluate normality with small sample sizes. 
C)  A scatter diagram of the whole data is always used to verify normality. 
D)  Errors are normally distributed when the scatter diagram shows a straight-line distribution.

Question 6

Which of the following is true of the R-squared (R2) value in Excel's Trendline function?

Accepted Answer

A)  A value of 1.0 for R2 indicates maximum deviation of the data from the line. 
B)  If the value of R2 is above 1.0, the line will be at a perfect fit for the data. 
C)  The value of R2 will always be between -1 and 1. 
D)  As the value of R2 gets higher, the line will be a better fit for the data. 
A)  A value of 1.0 for R2 indicates maximum deviation of the data from the line. 
B)  If the value of R2 is above 1.0, the line will be at a perfect fit for the data. 
C)  The value of R2 will always be between -1 and 1. 
D)  As the value of R2 gets higher, the line will be a better fit for the data.

Question 7

Regression models of data focus on predicting the future.

Accepted Answer

A)  missing 
B)  time-series 
C)  panel 
D)  cross-sectional 
A)  missing 
B)  time-series 
C)  panel 
D)  cross-sectional

Question 8

Which of the following helps in evaluation of autocorrelation?

Accepted Answer

A)  Breusch-Pagan test 
B)  Durbin-Watson statistic 
C)  Hosmer-Lemeshow test 
D)  Cochran-Mantel-Haenszel statistics 
A)  Breusch-Pagan test 
B)  Durbin-Watson statistic 
C)  Hosmer-Lemeshow test 
D)  Cochran-Mantel-Haenszel statistics

Question 9

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). $$\begin{array} { | l | l | } 
\hline\begin{array} { l } 
\text { Height } \
\text { (ft) }
\end{array} & \text { Cost (\$) } \
\hline 4 & 670 \
\hline 3 & 430 \
\hline 7 & 810 \
\hline 9 & 1100 \
\hline 6 & 790 \
\hline 8 & 880 \
\hline 5 & 760 \
\hline 11 & 1200 \
\hline
\end{array}$$
-Which of the following is true about Excel outputs Multiple R?

Accepted Answer

A)  It is often referred to as the coefficient of determination. 
B)  A value of 0 indicates positive correlation. 
C)  A negative slope of the regression line denotes a positive Multiple R. 
D)  It is another name for the sample correlation coefficient, r. 
A)  It is often referred to as the coefficient of determination. 
B)  A value of 0 indicates positive correlation. 
C)  A negative slope of the regression line denotes a positive Multiple R. 
D)  It is another name for the sample correlation coefficient, r.

Question 10

Interaction is:

Accepted Answer

A)  the principle of having a model with maximum explanatory variables. 
B)  the process of coding categorical variables. 
C)  a measure to determine the correlation between dependent variables. 
D)  the dependence between two independent variables. 
A)  the principle of having a model with maximum explanatory variables. 
B)  the process of coding categorical variables. 
C)  a measure to determine the correlation between dependent variables. 
D)  the dependence between two independent variables.

Question 11

Standard residuals:

Accepted Answer

A)  help detect outliers that may bias the results of a regression analysis. 
B)  cause differences in the regression equation by changing the slope and intercept. 
C)  point out the ranges for the population intercept and slope at a 95% confidence level. 
D)  provide information for testing hypothesis associated with the intercept and slope. 
A)  help detect outliers that may bias the results of a regression analysis. 
B)  cause differences in the regression equation by changing the slope and intercept. 
C)  point out the ranges for the population intercept and slope at a 95% confidence level. 
D)  provide information for testing hypothesis associated with the intercept and slope.

Question 12

In predictive analysis models, a second-order polynomial has only one hill or valley.

Accepted Answer

A) True 
 B)False

Question 13

When are logarithmic functions used in predictive analysis?

Accepted Answer

Logarithmic functions are used when the

Question 14

How many additional dummy variables are required if a categorical variable has 4 levels?

Accepted Answer

A)  2 
B)  3 
C)  1 
D)  4 
A)  2 
B)  3 
C)  1 
D)  4

Question 15

provide information about the unknown values of the true regression coefficients, accounting for sampling error.

Accepted Answer

A)  Standard errors 
B)  Confidence intervals 
C)  Adjusted R Squares 
D)  P-values 
A)  Standard errors 
B)  Confidence intervals 
C)  Adjusted R Squares 
D)  P-values

Question 16

For a simple linear regression model, significance of regression is:

Accepted Answer

A)  a measure of how well the regression line fits the data. 
B)  a hypothesis test of whether the true regression coefficient ß1 is zero. 
C)  a statistic that modifies the value of R2 by incorporating the sample size and the number of explanatory variables in the model. 
D)  the variability of the observed Y-values from the predicted values. 
A)  a measure of how well the regression line fits the data. 
B)  a hypothesis test of whether the true regression coefficient ß1 is zero. 
C)  a statistic that modifies the value of R2 by incorporating the sample size and the number of explanatory variables in the model. 
D)  the variability of the observed Y-values from the predicted values.

Question 17

A good regression model has the fewest number of explanatory variables providing an adequate interpretation of the dependent variable.

Accepted Answer

A) True 
 B)False

Question 18

For an independent variable Y, the error associated with the ith observation is:

Accepted Answer

A)  ei = Yi - Ŷi 
B)  Yi = (ei)2 - Ŷi 
C)  (Ŷi)2 ei = Yi 
D)  ei = (Yi + Ŷi)2 
A)  ei = Yi - Ŷi 
B)  Yi = (ei)2 - Ŷi 
C)  (Ŷi)2 ei = Yi 
D)  ei = (Yi + Ŷi)2

In a linear relationship, which of the following accounts for the many possible values of the dependent variable that vary around the mean?

What is the expected value for a 90 year-old piece of furniture?

Which of the following is true about the observed errors associated with estimating the value of the dependent variable using the regression line?

Which of the following is true when testing for normality of errors?

Which of the following is true of the R-squared (R2) value in Excel's Trendline function?

Regression models of data focus on predicting the future.

Which of the following helps in evaluation of autocorrelation?

Interaction is:

Standard residuals:

In predictive analysis models, a second-order polynomial has only one hill or valley.

When are logarithmic functions used in predictive analysis?

How many additional dummy variables are required if a categorical variable has 4 levels?

provide information about the unknown values of the true regression coefficients, accounting for sampling error.

For a simple linear regression model, significance of regression is:

A good regression model has the fewest number of explanatory variables providing an adequate interpretation of the dependent variable.

For an independent variable Y, the error associated with the ith observation is:

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 a linear relationship, which of the following accounts for the many possible values of the dependent variable that vary around the mean?

What is the expected value for a 90 year-old piece of furniture?

Which of the following is true about the observed errors associated with estimating the value of the dependent variable using the regression line?

Which of the following is true when testing for normality of errors?

Which of the following is true of the R-squared (R2) value in Excel's Trendline function?

Regression models of data focus on predicting the future.

Which of the following helps in evaluation of autocorrelation?

Interaction is:

Standard residuals:

In predictive analysis models, a second-order polynomial has only one hill or valley.

When are logarithmic functions used in predictive analysis?

How many additional dummy variables are required if a categorical variable has 4 levels?

provide information about the unknown values of the true regression coefficients, accounting for sampling error.

For a simple linear regression model, significance of regression is:

A good regression model has the fewest number of explanatory variables providing an adequate interpretation of the dependent variable.

For an independent variable Y, the error associated with the ith observation is:

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