Question 1

What is the predicted value ($\hat{y}$) when the numerical variable is x = 70 for the regression equation $\hat{y}$ = ?831 + 29.0x ? 0.113x2?

Accepted Answer

A)  1,967.7 
B)  1,454.2 
C)  645.3 
D)  Not enough sufficient data to calculate the predicted value. 
A)  1,967.7 
B)  1,454.2 
C)  645.3 
D)  Not enough sufficient data to calculate the predicted value. C

Question 2

Consider a linear regression model where y represents the response variable and d1 and d2 represent two dummy variables. The model is estimated as $\hat{y}$ = -2.49 + 1.86x + 3.20d1 - 2.21d2 + 0.50d1d2. Compute $\hat{y}$ for x = 3, d1 = 1, and d2 = 0.

Accepted Answer

A)  13.61 
B)  3.42 
C)  13.17 
D)  6.29 
A)  13.61 
B)  3.42 
C)  13.17 
D)  6.29 D

Question 3

Using a sample of 50, the following regression output is obtained from estimating the linear probability regression model y = $\beta$0 +$\beta$1x + $\varepsilon$. What is the predicted probability when x = 14?

Accepted Answer

A)  4.42 
B)  3.86 
C)  8.34 
D)  0.72 
A)  4.42 
B)  3.86 
C)  8.34 
D)  0.72 B

Question 4

In the k-fold cross-validation method, the smaller the k value, the greater the reliability of the k-fold method.

Accepted Answer

A) True 
 B)False

Question 5

Consider a binary response variable y and a predictor variable x that varies between 0 and 5. The linear model is estimated as   = -2.90 + 0.65x. What is the estimated probability for x = 5?

Accepted Answer

A)  0.35 
B)  6.15 
C)  0.65 
D)  -6.15 
A)  0.35 
B)  6.15 
C)  0.65 
D)  -6.15

Question 6

The following table contains the parameter estimates of the linear probability regression model and the logistic regression model. When considering a binary response variable y and two predictor variables, x1 and x2, What is the estimated linear probability implied by the logistic probability regression model for x1 = 3 with x2 = 9?

Accepted Answer

A)  1.87 
B)  0.35 
C)  0.64 
D)  -0.87 
A)  1.87 
B)  0.35 
C)  0.64 
D)  -0.87

Question 7

Which interpretation of a linear probability regression model represents the estimate ^P = -40 + 0.05x?

Accepted Answer

A)  for each 1 unit increase in x, the predicted probability ^P decreases by 0.05. 
B)  for each 40 unit decrease in x, the predicted probability ^P increases by 40. 
C)  for each 1 unit increase in x, the predicted probability ^P increases by 0.05. 
D)  for each 0.05 unit increase in x, the predicted probability ^P decreases by 40. 
A)  for each 1 unit increase in x, the predicted probability ^P decreases by 0.05. 
B)  for each 40 unit decrease in x, the predicted probability ^P increases by 40. 
C)  for each 1 unit increase in x, the predicted probability ^P increases by 0.05. 
D)  for each 0.05 unit increase in x, the predicted probability ^P decreases by 40.

Question 8

In the following logarithmic regression model, $\beta$1 × 0.01 measures the approximate unit change in E(y) when x increases. If $\beta$1 = 6,000, then what is the unit change in E(y)?

Accepted Answer

A)  6 units 
B)  600 units 
C)  60 units 
D)  6,000 units 
A)  6 units 
B)  600 units 
C)  60 units 
D)  6,000 units

Question 9

In determining the partial effect on dummy variable d in a regression model with an interaction variable $\hat{y}$  = b0 + b1x + b2d + b3xd, the numeric variable x value needs to be known.

Accepted Answer

A) True 
 B)False

Question 10

Christian Za is using the holdout method to partition his data into two independent and mutually exclusive sets: 75% in a training set and 25% in a validation set. Based on an R2 value of 0.4532 and RMSE of 0.3268 for the training set and an R2 value of 0.1426 and RMSE of 0.5371 for the validation set, which of the competing models is the preferred model?

Accepted Answer

A)  The validation data set would be the preferred model. 
B)  The training data set would be the preferred model. 
C)  Both models are preferred models. 
D)  There is not enough data to determine the preferred model. 
A)  The validation data set would be the preferred model. 
B)  The training data set would be the preferred model. 
C)  Both models are preferred models. 
D)  There is not enough data to determine the preferred model.

Question 11

Consider a linear regression model where y represents the response variable and d1 and d2 represent two dummy variables. The model is estimated as $\hat{y}$ = -2.53 + 2.04x + 4.20d1 - 2.86d2 + 0.68d1d2. Compute $\hat{y}$ for x = 3, d1 = 1, and d2 = 0.

Accepted Answer

A)  13.53 
B)  3.71 
C)  12.85 
D)  7.79 
A)  13.53 
B)  3.71 
C)  12.85 
D)  7.79

Question 12

Because software packages use random draws of the observations to partition data, the results will not be identical to a fixed partitioning of the observations.

Accepted Answer

A) True 
 B)False

Question 13

In using both the linear probability regression and the logistic regression models for n = 40, the following table is the analysis of the holdout method. Based on the table, what is the impact of changing the $\hat{y}$  to binary predictions?

Accepted Answer

A)  There is no relevant outcome changing ? to a binary variable. 
B)  By comparing $\hat{y}$  to y, the validation results are not able to be validated. 
C)  By comparing $\hat{y}$  to y, the accuracy of the model is 100%, but can change with a larger validation set. 
D)  By comparing $\hat{y}$  to y, the accuracy of the model is 100% and will not change based on a larger validation set. 
A)  There is no relevant outcome changing ? to a binary variable. 
B)  By comparing $\hat{y}$  to y, the validation results are not able to be validated. 
C)  By comparing $\hat{y}$  to y, the accuracy of the model is 100%, but can change with a larger validation set. 
D)  By comparing $\hat{y}$  to y, the accuracy of the model is 100% and will not change based on a larger validation set.

Question 14

ln(y) = $\beta$0 + $\beta$1 ln(x) + $\varepsilon$ represents the exponential regression model.

Accepted Answer

A) True 
 B)False

Question 15

In the linear probability regression model, the response variable y will equal 1 or 0 to represent the probability of success.

Accepted Answer

A) True 
 B)False

Question 16

Sam, a marketing manager for XYZ big box stores, is trying to determine if there is a relationship between shelf space (in feet) and sales (in hundreds of dollars). To do this, Sam selected the top 12 producing locations. Using the provided Model 1 results, which option best interprets the impact of the coefficients and p-values?
  
* the p-value

Accepted Answer

A)  The predictor variables are below one offering a negative correlation. 
B)  All predictor variables are positive with a significant influence on sales. 
C)  There is no impact presented with the coefficients and p-value results. 
D)  The p-value offers significant influence where the coefficient provides reduction on sales. 
A)  The predictor variables are below one offering a negative correlation. 
B)  All predictor variables are positive with a significant influence on sales. 
C)  There is no impact presented with the coefficients and p-value results. 
D)  The p-value offers significant influence where the coefficient provides reduction on sales.

Question 17

The estimate regression equation for the average cost of widgets is:
^Widgets = 7.23 - 0.2478 Output + 0.1954 Output2. Both predictor variables are statistically significant at 5% level, confirming the quadratic effect. What is the predictive average from an output level of 2 million units to 3 million units?

Accepted Answer

A)  The increase in output units results in a $0.02 decrease in predictive average cost. 
B)  The increase in output units results in a $0.24 increase in predictive average cost. 
C)  The increase in output units results in a $0.73 decrease in predictive average cost. 
D)  The increase in output units results in a $0.73 increase in predictive average cost. 
A)  The increase in output units results in a $0.02 decrease in predictive average cost. 
B)  The increase in output units results in a $0.24 increase in predictive average cost. 
C)  The increase in output units results in a $0.73 decrease in predictive average cost. 
D)  The increase in output units results in a $0.73 increase in predictive average cost.

Question 18

The estimate regression equation for the average cost of widgets is:
^Widgets = 7.33 - 0.2340 Output + 0.1892 Output2. Both predictor variables are statistically significant at 5% level, confirming the quadratic effect. What is the predictive average from an output level of 2 million units to 3 million units?

Accepted Answer

A)  The increase in output units results in a $0.02 decrease in predictive average cost. 
B)  The increase in output units results in a $0.24 increase in predictive average cost. 
C)  The increase in output units results in a $0.71 decrease in predictive average cost. 
D)  The increase in output units results in a $0.71 increase in predictive average cost. 
A)  The increase in output units results in a $0.02 decrease in predictive average cost. 
B)  The increase in output units results in a $0.24 increase in predictive average cost. 
C)  The increase in output units results in a $0.71 decrease in predictive average cost. 
D)  The increase in output units results in a $0.71 increase in predictive average cost.

Question 19

Consider the sample correlation coefficients in the table below. How much of the variability in time can be explained by boxes using the alternative way of to determine the coefficient of determination.

Accepted Answer

A)  79% 
B)  26.0% 
C)  49.4% 
D)  57.0% 
A)  79% 
B)  26.0% 
C)  49.4% 
D)  57.0%

Question 20

To test for the accuracy rate in a binary choice model, the number of correct classification observations for both outcomes should be reported.

Accepted Answer

A) True 
 B)False

What is the predicted value ( $\hat{y}$ ) when the numerical variable is x = 70 for the regression equation $\hat{y}$ = ?831 + 29.0x ? 0.113x²?

Consider a linear regression model where y represents the response variable and d₁ and d₂ represent two dummy variables. The model is estimated as $\hat{y}$ = -2.49 + 1.86x + 3.20d₁ - 2.21d₂ + 0.50d₁d₂. Compute $\hat{y}$ for x = 3, d₁ = 1, and d₂ = 0.

In the k-fold cross-validation method, the smaller the k value, the greater the reliability of the k-fold method.

Consider a binary response variable y and a predictor variable x that varies between 0 and 5. The linear model is estimated as = -2.90 + 0.65x. What is the estimated probability for x = 5?

Which interpretation of a linear probability regression model represents the estimate ^P = -40 + 0.05x?

In the following logarithmic regression model, $\beta$ ₁ × 0.01 measures the approximate unit change in E(y) when x increases. If $\beta$ ₁= 6,000, then what is the unit change in E(y)?

In determining the partial effect on dummy variable d in a regression model with an interaction variable $\hat{y}$ = b₀ + b₁x + b₂d + b₃xd, the numeric variable x value needs to be known.

Consider a linear regression model where y represents the response variable and d₁ and d₂ represent two dummy variables. The model is estimated as $\hat{y}$ = -2.53 + 2.04x + 4.20d₁ - 2.86d₂ + 0.68d₁d₂. Compute $\hat{y}$ for x = 3, d₁ = 1, and d₂ = 0.

Because software packages use random draws of the observations to partition data, the results will not be identical to a fixed partitioning of the observations.

ln(y) = $\beta$ ₀ + $\beta$ ₁ ln(x) + $\varepsilon$ represents the exponential regression model.

In the linear probability regression model, the response variable y will equal 1 or 0 to represent the probability of success.

Consider the sample correlation coefficients in the table below. How much of the variability in time can be explained by boxes using the alternative way of to determine the coefficient of determination.

To test for the accuracy rate in a binary choice model, the number of correct classification observations for both outcomes should be reported.

Introduction to Business Analytics

Data Management and Wrangling

Data Visualization and Summary Measures

Probability and Probability Distributions

Statistical Inference

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 7: Advanced Regression Analysis

What is the predicted value ( y^\hat{y}y^​ ) when the numerical variable is x = 70 for the regression equation y^\hat{y}y^​ = ?831 + 29.0x ? 0.113x2?

Consider a linear regression model where y represents the response variable and d1 and d2 represent two dummy variables. The model is estimated as y^\hat{y}y^​ = -2.49 + 1.86x + 3.20d1 - 2.21d2 + 0.50d1d2. Compute y^\hat{y}y^​ for x = 3, d1 = 1, and d2 = 0.

Using a sample of 50, the following regression output is obtained from estimating the linear probability regression model y = β\betaβ 0 + β\betaβ 1x + ε\varepsilonε . What is the predicted probability when x = 14?

In the k-fold cross-validation method, the smaller the k value, the greater the reliability of the k-fold method.

Consider a binary response variable y and a predictor variable x that varies between 0 and 5. The linear model is estimated as = -2.90 + 0.65x. What is the estimated probability for x = 5?

Which interpretation of a linear probability regression model represents the estimate ^P = -40 + 0.05x?

In the following logarithmic regression model, β\betaβ 1 × 0.01 measures the approximate unit change in E(y) when x increases. If β\betaβ 1 = 6,000, then what is the unit change in E(y)?

In determining the partial effect on dummy variable d in a regression model with an interaction variable y^\hat{y}y^​ = b0 + b1x + b2d + b3xd, the numeric variable x value needs to be known.

Consider a linear regression model where y represents the response variable and d1 and d2 represent two dummy variables. The model is estimated as y^\hat{y}y^​ = -2.53 + 2.04x + 4.20d1 - 2.86d2 + 0.68d1d2. Compute y^\hat{y}y^​ for x = 3, d1 = 1, and d2 = 0.

Because software packages use random draws of the observations to partition data, the results will not be identical to a fixed partitioning of the observations.

In using both the linear probability regression and the logistic regression models for n = 40, the following table is the analysis of the holdout method. Based on the table, what is the impact of changing the y^\hat{y}y^​ to binary predictions?

ln(y) = β\betaβ 0 + β\betaβ 1 ln(x) + ε\varepsilonε represents the exponential regression model.

In the linear probability regression model, the response variable y will equal 1 or 0 to represent the probability of success.

Consider the sample correlation coefficients in the table below. How much of the variability in time can be explained by boxes using the alternative way of to determine the coefficient of determination.

To test for the accuracy rate in a binary choice model, the number of correct classification observations for both outcomes should be reported.

Introduction to Business Analytics

Data Management and Wrangling

Data Visualization and Summary Measures

Probability and Probability Distributions

Statistical Inference

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

What is the predicted value ( $\hat{y}$ ) when the numerical variable is x = 70 for the regression equation $\hat{y}$ = ?831 + 29.0x ? 0.113x²?

Consider a linear regression model where y represents the response variable and d₁ and d₂ represent two dummy variables. The model is estimated as $\hat{y}$ = -2.49 + 1.86x + 3.20d₁ - 2.21d₂ + 0.50d₁d₂. Compute $\hat{y}$ for x = 3, d₁ = 1, and d₂ = 0.

In the following logarithmic regression model, $\beta$ ₁ × 0.01 measures the approximate unit change in E(y) when x increases. If $\beta$ ₁= 6,000, then what is the unit change in E(y)?

In determining the partial effect on dummy variable d in a regression model with an interaction variable $\hat{y}$ = b₀ + b₁x + b₂d + b₃xd, the numeric variable x value needs to be known.

Consider a linear regression model where y represents the response variable and d₁ and d₂ represent two dummy variables. The model is estimated as $\hat{y}$ = -2.53 + 2.04x + 4.20d₁ - 2.86d₂ + 0.68d₁d₂. Compute $\hat{y}$ for x = 3, d₁ = 1, and d₂ = 0.

ln(y) = $\beta$ ₀ + $\beta$ ₁ ln(x) + $\varepsilon$ represents the exponential regression model.