9/1/25, 7:51 PM Homework 2: Quiz: Regression Analysis - ISYE-6414-OAN/Q2/QIS/ASY
Homework 2: Quiz
Due Jun 8 at 11:59pm
Points 40
Questions 35
Available May 26 at 8am - Jun 17 at 11:59pm
Time Limit None
Instructions
Due: Jun 8 at 11:59 p.m. Eastern
Attention: You only have ONE attempt to answer the questions.
This quiz is no longer available as the course has been concluded.
Attempt History
Attempt Time Score
LATEST Attempt 1 4,713 minutes 32.2 out of 40
(D Correct answers are no longer available.
Score for this quiz: 32.2 out of 40
Submitted Jun 8 at 6:58pm
This attempt took 4,713 minutes.
Incorrect
Question 1
0/1pts
In linear regression, goodness of fit describes:
how well the predictors explain the response.
how well the model predicts the response variable for unseen data.
how well the data fit the assumptions of the model.
how well the regression line fits the observed data.
both a and d
how well the data fit the assumptions of the model.
2.8.6
. 1/20
,9/1/25, 7:51 PM Homework 2: Quiz: Regression Analysis - ISYE-6414-OAN/Q2/QIS/ASY
Please note that these are not Goodness-of-fit measures. Goodness-of-fit refers to the goodness of the
fit of the data with the model structure and assumptions. The measures on this slide can be used to
evaluate the performance of how well the predicting variables explain the response under the linearity
assumption.
Incorrect
Question 2
0/1pts
In multiple linear regression, the parameters of the model are :
the variable coefficients
the intercept coefficient
the variance of the errors (sigma squared))
a and b only
a.,b.andc.
a., b. and c.
2.1.3 Fundamentals, Objectives and Examples
"In the linear regression model, the parameters defining the regression line, the regression coefficients,
Bo, B4 through B,, are unknown parameters. We have an additional parameter, the variance of the
errors, denoted with sigma squared. Model parameters are unknown regardless of how much data we
observe. But we can derive some approximations or estimates of the parameters given the data and the
model assumptions "
Incorrect
Question 3
0/1pts
If a multiple linear regression model contains 5 quantitative predicting variables and an intercept, then
the number of parameters to estimate is .
W
B~
O
OO
N~
2.1. Fundamentals, Objectives and Examples
The model parameters are Bo, B1, ..., B5, and o?. Thus, there are 7 parameters.
. 2/20
, 9/1/25, 7:51 PM Homework 2: Quiz: Regression Analysis - ISYE-6414-OAN/Q2/QIS/ASY
Partial
Question 4
0.9 1 1.5 pts
Select all answers which correspond to common measures of predicting accuracy in multiple linear
regression.
mean squared prediction error (MSPE)
mean absolute prediction error (MAE)
precision measure (PM) or precision error
mean absolute percentage error (MAPE)
confidence interval measure (CIM)
[2.8. Model Evaluation and Multicollinearity, Slide 8]
The most common reported measures of predicting accuracy are on this slide:
• Mean squared prediction error abbreviated MSPE and computed as the mean of the squared
differences between predicted and observed;
• Mean absolute prediction errors abbreviated MAE and computed as the mean of the absolute values
of the differences between predicted and observed;
• Mean absolute percentage error abbreviated MAPE and computed as the mean of the absolute
values of the differences scaled by the observed responses;
• Precision measure (or precision error) abbreviated PM here and computed as the ratio between
MSPE and the sum of square differences between the response and the mean of the responses;
• Confidence Interval Measure abbreviated here as CIM and computed as the number of predictions
falling outside of the prediction intervals divided by the number of predictions made.
Question 5
pts
In multiple linear regression, the independence assumption cannot be tested through visual analysis, but
can be approximated by visually assessing residuals for .
normality
linearity
constant variance
correlation
mean zero
correlation
2.7.10
https://gatech.instructure.com/courses/469948/quizzes/662799?module_item_id=4920362 3/20
Homework 2: Quiz
Due Jun 8 at 11:59pm
Points 40
Questions 35
Available May 26 at 8am - Jun 17 at 11:59pm
Time Limit None
Instructions
Due: Jun 8 at 11:59 p.m. Eastern
Attention: You only have ONE attempt to answer the questions.
This quiz is no longer available as the course has been concluded.
Attempt History
Attempt Time Score
LATEST Attempt 1 4,713 minutes 32.2 out of 40
(D Correct answers are no longer available.
Score for this quiz: 32.2 out of 40
Submitted Jun 8 at 6:58pm
This attempt took 4,713 minutes.
Incorrect
Question 1
0/1pts
In linear regression, goodness of fit describes:
how well the predictors explain the response.
how well the model predicts the response variable for unseen data.
how well the data fit the assumptions of the model.
how well the regression line fits the observed data.
both a and d
how well the data fit the assumptions of the model.
2.8.6
. 1/20
,9/1/25, 7:51 PM Homework 2: Quiz: Regression Analysis - ISYE-6414-OAN/Q2/QIS/ASY
Please note that these are not Goodness-of-fit measures. Goodness-of-fit refers to the goodness of the
fit of the data with the model structure and assumptions. The measures on this slide can be used to
evaluate the performance of how well the predicting variables explain the response under the linearity
assumption.
Incorrect
Question 2
0/1pts
In multiple linear regression, the parameters of the model are :
the variable coefficients
the intercept coefficient
the variance of the errors (sigma squared))
a and b only
a.,b.andc.
a., b. and c.
2.1.3 Fundamentals, Objectives and Examples
"In the linear regression model, the parameters defining the regression line, the regression coefficients,
Bo, B4 through B,, are unknown parameters. We have an additional parameter, the variance of the
errors, denoted with sigma squared. Model parameters are unknown regardless of how much data we
observe. But we can derive some approximations or estimates of the parameters given the data and the
model assumptions "
Incorrect
Question 3
0/1pts
If a multiple linear regression model contains 5 quantitative predicting variables and an intercept, then
the number of parameters to estimate is .
W
B~
O
OO
N~
2.1. Fundamentals, Objectives and Examples
The model parameters are Bo, B1, ..., B5, and o?. Thus, there are 7 parameters.
. 2/20
, 9/1/25, 7:51 PM Homework 2: Quiz: Regression Analysis - ISYE-6414-OAN/Q2/QIS/ASY
Partial
Question 4
0.9 1 1.5 pts
Select all answers which correspond to common measures of predicting accuracy in multiple linear
regression.
mean squared prediction error (MSPE)
mean absolute prediction error (MAE)
precision measure (PM) or precision error
mean absolute percentage error (MAPE)
confidence interval measure (CIM)
[2.8. Model Evaluation and Multicollinearity, Slide 8]
The most common reported measures of predicting accuracy are on this slide:
• Mean squared prediction error abbreviated MSPE and computed as the mean of the squared
differences between predicted and observed;
• Mean absolute prediction errors abbreviated MAE and computed as the mean of the absolute values
of the differences between predicted and observed;
• Mean absolute percentage error abbreviated MAPE and computed as the mean of the absolute
values of the differences scaled by the observed responses;
• Precision measure (or precision error) abbreviated PM here and computed as the ratio between
MSPE and the sum of square differences between the response and the mean of the responses;
• Confidence Interval Measure abbreviated here as CIM and computed as the number of predictions
falling outside of the prediction intervals divided by the number of predictions made.
Question 5
pts
In multiple linear regression, the independence assumption cannot be tested through visual analysis, but
can be approximated by visually assessing residuals for .
normality
linearity
constant variance
correlation
mean zero
correlation
2.7.10
https://gatech.instructure.com/courses/469948/quizzes/662799?module_item_id=4920362 3/20
