Question 1

Based on goodness-of-fit measures, which is the preferred model based on the results below:

Accepted Answer

A)  model 1 
B)  model 2 
C)  model 3 
D)  Both model 1 & model 2 
A)  model 1 
B)  model 2 
C)  model 3 
D)  Both model 1 & model 2 C

Question 2

Quantitative variables are numeric, whereas qualitative variables are descriptors reflecting categories.

Accepted Answer

A) True 
 B)False  True

Question 3

Analysis of covariance (ANCOVA) is used in the context of linear regression to derive R2.

Accepted Answer

A) True 
 B)False  False

Question 4

The response variable is the outcome of a variable, whereas the predictor is the input variable(s).

Accepted Answer

A) True 
 B)False

Question 5

Based on the following table, what is the sample regression equation?

Accepted Answer

A)  Earnings = 10,625.6413 + 0.3731Cost + 174.0756Grad + 127.3845Debt 
B)  Earnings = 10,625.6413 + 0.373Cost + 174.0756Grad ? 127.385Debt 
C)  Earnings = 10,625.6413 ? 0.373Cost + 174.0756Grad ? 127.385Debt 
D)  Earnings = 10,625.6413 ? 0.373Cost + 174.0756Grad + 127.385 Debt 
A)  Earnings = 10,625.6413 + 0.3731Cost + 174.0756Grad + 127.3845Debt 
B)  Earnings = 10,625.6413 + 0.373Cost + 174.0756Grad ? 127.385Debt 
C)  Earnings = 10,625.6413 ? 0.373Cost + 174.0756Grad ? 127.385Debt 
D)  Earnings = 10,625.6413 ? 0.373Cost + 174.0756Grad + 127.385 Debt

Question 6

The standard error of the estimate is Se =   . What result best fits the sample data?< / p>

Accepted Answer

A)  when Se = 1.526 
B)  when Se = 2.001 
C)  when Se = 0.543 
D)  when Se = 0.005 
A)  when Se = 1.526 
B)  when Se = 2.001 
C)  when Se = 0.543 
D)  when Se = 0.005

Question 7

If $\hat{y}$ = 120 - 3x with y = product and x = price of product, what happens to the demand if the price is increased by 2 units?

Accepted Answer

A)  increases by 6 units 
B)  increases by 9 units 
C)  decreases by 6 units 
D)  decreases by 9 units 
A)  increases by 6 units 
B)  increases by 9 units 
C)  decreases by 6 units 
D)  decreases by 9 units

Question 8

Which of the following is not a goodness-of-fit measure?

Accepted Answer

A)  adjusted coefficient of determination 
B)  coefficient of determination 
C)  standard error of the estimate 
D)  simple regression model 
A)  adjusted coefficient of determination 
B)  coefficient of determination 
C)  standard error of the estimate 
D)  simple regression model

Question 9

Conduct a test to determine if the predictor variables are jointly significant in explaining Earnings at $\alpha$ = 0.05.

Accepted Answer

A)  P(F1,18) $\ge$74.608, p-value is less than $\alpha$ = 0.05, we reject H0, but jointly significant in explaining earnings. 
B)  P(F1,19) $\ge$ 82.818, p-value is less than $\alpha$ = 0.05, we accept H0, jointly do not explain earnings. 
C)  P(F1,18) $\ge$ 74.608, p-value is less than $\alpha$ = 0.05, we accept H0, but jointly significant in explaining earnings. 
D)  P(F1,19) $\ge$ 82.818, p-value is less than $\alpha$ = 0.05, we reject H0, jointly do not explain earnings. 
A)  P(F1,18) $\ge$74.608, p-value is less than $\alpha$ = 0.05, we reject H0, but jointly significant in explaining earnings. 
B)  P(F1,19) $\ge$ 82.818, p-value is less than $\alpha$ = 0.05, we accept H0, jointly do not explain earnings. 
C)  P(F1,18) $\ge$ 74.608, p-value is less than $\alpha$ = 0.05, we accept H0, but jointly significant in explaining earnings. 
D)  P(F1,19) $\ge$ 82.818, p-value is less than $\alpha$ = 0.05, we reject H0, jointly do not explain earnings.

Question 10

In a linear regression, $\varepsilon$, read as epsilon, is

Accepted Answer

A)  a dummy variable. 
B)  the unrounded number. 
C)  the random error. 
D)  is the relationship between variables. 
A)  a dummy variable. 
B)  the unrounded number. 
C)  the random error. 
D)  is the relationship between variables.

Question 11

Regression analysis captures the relationship between only two distinct variables.

Accepted Answer

A) True 
 B)False

Question 12

Camber Seal is a financial planner hired to review KMB stock. She is considering the CAPM where the KMB risk-adjusted stock return R - Rf is used as the response variable and the risk-adjusted market return Rm - R f is used as the predictor variable. KMB stock is considered staple products, whether the economy is good or bad. Given estimates for the beta coefficient is 0.7528, standard error of 0.1600, and a p-value of 0.028 with a formulated hypothesis of H 0  : $\beta$ $\ge$ 1 H A : $\beta$ < 1. At a 5% significance level, what is the risk determination of the stock against the market?

Accepted Answer

A)  $\beta$ is significantly less than one, thus, H0 : is rejected and less risky than the market. 
B)  $\beta$ is significantly higher than one, thus, H0 : is accepted and less risky than the market. 
C)  $\beta$ is significantly less than one, thus, H0 : is accepted and riskier than the market. 
D)  $\beta$ is significantly higher than one, thus, H0 : is rejected and riskier than the market. 
A)  $\beta$ is significantly less than one, thus, H0 : is rejected and less risky than the market. 
B)  $\beta$ is significantly higher than one, thus, H0 : is accepted and less risky than the market. 
C)  $\beta$ is significantly less than one, thus, H0 : is accepted and riskier than the market. 
D)  $\beta$ is significantly higher than one, thus, H0 : is rejected and riskier than the market.

Question 13

In the following equation $\hat{y}$ = 40,000 + 2x with given sales (y in $500) and marketing (x in dollars), what does the equation imply?

Accepted Answer

A)  An increase of $2 in marketing is associated with an increase of $41,000 in sales. 
B)  An increase of $1 in marketing is associated with an increase of $1,000 in sales. 
C)  An increase of $2 in marketing is associated with an increase of $1,000 in sales. 
D)  An increase of $1 in marketing is associated with an increase of $41,000 in sales. 
A)  An increase of $2 in marketing is associated with an increase of $41,000 in sales. 
B)  An increase of $1 in marketing is associated with an increase of $1,000 in sales. 
C)  An increase of $2 in marketing is associated with an increase of $1,000 in sales. 
D)  An increase of $1 in marketing is associated with an increase of $41,000 in sales.

Question 14

When determining if there is evidence of a linear relationship between variables, OLS estimators must be __________ for the test to be valid.

Accepted Answer

A)  normally distributed 
B)  scattered 
C)  a single variable 
D)  predicted 
A)  normally distributed 
B)  scattered 
C)  a single variable 
D)  predicted

Question 15

Which one of the following is not a common violation in the test of validity?

Accepted Answer

A)  estimation 
B)  multicollinearity 
C)  changing variability 
D)  nonlinear patterns 
A)  estimation 
B)  multicollinearity 
C)  changing variability 
D)  nonlinear patterns

Question 16

It is important to review residual plots to identify any signs of _____ and correlated observations in cross-sectional and time-series studies.

Accepted Answer

A)  variable studies 
B)  residual plot crosses 
C)  changing variability 
D)  standard error 
A)  variable studies 
B)  residual plot crosses 
C)  changing variability 
D)  standard error

Question 17

Based on the following table, what is the sample regression equation?

Accepted Answer

A)  Earnings = 10,004.9665 + 0.4349Cost + 178.0989Grad + 141.4783Debt 
B)  Earnings = 10,004.9665 ? 0.4349Cost + 178.0989Grad + 141.4783Debt 
C)  Earnings = 10,004.9665 + 0.4349Cost + 178.0989Grad ? 141.4783Debt 
D)  Earnings = 10,004.9665 ? 0.4349Cost + 178.0989Grad ? 141.4783 Debt 
A)  Earnings = 10,004.9665 + 0.4349Cost + 178.0989Grad + 141.4783Debt 
B)  Earnings = 10,004.9665 ? 0.4349Cost + 178.0989Grad + 141.4783Debt 
C)  Earnings = 10,004.9665 + 0.4349Cost + 178.0989Grad ? 141.4783Debt 
D)  Earnings = 10,004.9665 ? 0.4349Cost + 178.0989Grad ? 141.4783 Debt

Question 18

In a simple linear regression based on 20 observations, it is found b1 = 3.25 and se(b1) = 1.35. Consider the hypothesis: H0 : $\beta$1 $\neq$ 0 and HA : $\beta$1 $\neq$ 0 . Calculate the value of the test statistic.

Accepted Answer

A)  2.41 
B)  0.415 
C)  1.42 
D)  0.121 
A)  2.41 
B)  0.415 
C)  1.42 
D)  0.121

Question 19

In a study where the least squares estimates were based on 34 sets of sample observations, the total sum of squares and regression sum of squares were found to be: SST = 4.53 and SSR = 4.21. What is the error sum of squares?

Accepted Answer

A)  1.07 
B)  0.32 
C)  0.929 
D)  8.74 
A)  1.07 
B)  0.32 
C)  0.929 
D)  8.74

Question 20

In the goodness-of-fit measures, interpret the coefficient of determination for Earnings with Model 3 and what the sample variation of earnings explains.

Accepted Answer

A)  93.3700% of the sample variation in Earnings is explained by the regression model. 
B)  0.8475 of the sample variation in Earnings determines the model selection. 
C)  0.01 of the sample variation in Earnings determines the model selection. 
D)  71.77% of the sample variation in Earnings is explained by the regression model. 
A)  93.3700% of the sample variation in Earnings is explained by the regression model. 
B)  0.8475 of the sample variation in Earnings determines the model selection. 
C)  0.01 of the sample variation in Earnings determines the model selection. 
D)  71.77% of the sample variation in Earnings is explained by the regression model.

Based on goodness-of-fit measures, which is the preferred model based on the results below:

Quantitative variables are numeric, whereas qualitative variables are descriptors reflecting categories.

Analysis of covariance (ANCOVA) is used in the context of linear regression to derive R².

The response variable is the outcome of a variable, whereas the predictor is the input variable(s).

Based on the following table, what is the sample regression equation?

The standard error of the estimate is S_e = . What result best fits the sample data?< / p>

If $\hat{y}$ = 120 - 3x with y = product and x = price of product, what happens to the demand if the price is increased by 2 units?

Which of the following is not a goodness-of-fit measure?

Conduct a test to determine if the predictor variables are jointly significant in explaining Earnings at $\alpha$ = 0.05. $Conduct a test to determine if the predictor variables are jointly significant in explaining Earnings at \alpha = 0.05.$

In a linear regression, $\varepsilon$ , read as epsilon, is

Regression analysis captures the relationship between only two distinct variables.

In the following equation $\hat{y}$ = 40,000 + 2x with given sales (y in $500) and marketing (x in dollars), what does the equation imply?

When determining if there is evidence of a linear relationship between variables, OLS estimators must be __________ for the test to be valid.

Which one of the following is not a common violation in the test of validity?

It is important to review residual plots to identify any signs of _____ and correlated observations in cross-sectional and time-series studies.

Based on the following table, what is the sample regression equation?

In a simple linear regression based on 20 observations, it is found b₁= 3.25 and se(b₁) = 1.35. Consider the hypothesis: H₀ : $\beta$ ₁ $\neq$ 0and H_A: $\beta$ ₁ $\neq$ 0.Calculate the value of the test statistic.

In a study where the least squares estimates were based on 34 sets of sample observations, the total sum of squares and regression sum of squares were found to be: SST = 4.53 and SSR = 4.21. What is the error sum of squares?

In the goodness-of-fit measures, interpret the coefficient of determination for Earnings with Model 3 and what the sample variation of earnings explains.

Introduction to Business Analytics

Data Management and Wrangling

Data Visualization and Summary Measures

Probability and Probability Distributions

Statistical Inference

Advanced Regression Analysis

Introduction to Data Mining

Supervised Data Mining: K-Nearest Neighbors and Naãve Bayes

Supervised Data Mining: Decision Trees

Unsupervised Data Mining

Forecasting With Time Series Data

Introduction to Prescriptive Analytics

Filters

Exam 6: Regression Analysis

Based on goodness-of-fit measures, which is the preferred model based on the results below:

Quantitative variables are numeric, whereas qualitative variables are descriptors reflecting categories.

Analysis of covariance (ANCOVA) is used in the context of linear regression to derive R2.

The response variable is the outcome of a variable, whereas the predictor is the input variable(s).

Based on the following table, what is the sample regression equation?

The standard error of the estimate is Se = . What result best fits the sample data?< / p>

If y^\hat{y}y^​ = 120 - 3x with y = product and x = price of product, what happens to the demand if the price is increased by 2 units?

Which of the following is not a goodness-of-fit measure?

Conduct a test to determine if the predictor variables are jointly significant in explaining Earnings at α\alphaα = 0.05.

In a linear regression, ε\varepsilonε , read as epsilon, is

Regression analysis captures the relationship between only two distinct variables.

In the following equation y^\hat{y}y^​ = 40,000 + 2x with given sales (y in $500) and marketing (x in dollars), what does the equation imply?

When determining if there is evidence of a linear relationship between variables, OLS estimators must be __________ for the test to be valid.

Which one of the following is not a common violation in the test of validity?

It is important to review residual plots to identify any signs of _____ and correlated observations in cross-sectional and time-series studies.

Based on the following table, what is the sample regression equation?

In a simple linear regression based on 20 observations, it is found b1 = 3.25 and se(b1) = 1.35. Consider the hypothesis: H0 : β\betaβ 1 ≠\neq= 0 and HA : β\betaβ 1 ≠\neq= 0 . Calculate the value of the test statistic.

In a study where the least squares estimates were based on 34 sets of sample observations, the total sum of squares and regression sum of squares were found to be: SST = 4.53 and SSR = 4.21. What is the error sum of squares?

In the goodness-of-fit measures, interpret the coefficient of determination for Earnings with Model 3 and what the sample variation of earnings explains.

Introduction to Business Analytics

Data Management and Wrangling

Data Visualization and Summary Measures

Probability and Probability Distributions

Statistical Inference

Advanced Regression Analysis

Introduction to Data Mining

Supervised Data Mining: K-Nearest Neighbors and Naãve Bayes

Supervised Data Mining: Decision Trees

Unsupervised Data Mining

Forecasting With Time Series Data

Introduction to Prescriptive Analytics

Filters

Analysis of covariance (ANCOVA) is used in the context of linear regression to derive R².

The standard error of the estimate is S_e = . What result best fits the sample data?< / p>

If $\hat{y}$ = 120 - 3x with y = product and x = price of product, what happens to the demand if the price is increased by 2 units?

Conduct a test to determine if the predictor variables are jointly significant in explaining Earnings at $\alpha$ = 0.05. $Conduct a test to determine if the predictor variables are jointly significant in explaining Earnings at \alpha = 0.05.$

In a linear regression, $\varepsilon$ , read as epsilon, is

In the following equation $\hat{y}$ = 40,000 + 2x with given sales (y in $500) and marketing (x in dollars), what does the equation imply?

In a simple linear regression based on 20 observations, it is found b₁= 3.25 and se(b₁) = 1.35. Consider the hypothesis: H₀ : $\beta$ ₁ $\neq$ 0and H_A: $\beta$ ₁ $\neq$ 0.Calculate the value of the test statistic.