6/13/25, 9:10 AM 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 8 at 11:59pm
Time Limit None
Instructions
Due: Jun 8 at 11:59 p.m. Eastern
: You only have attempt to answer the questions.
Attention ONE
This quiz was locked Jun 8 at 11:59pm.
Attempt History
Attempt Time
Score for this quiz: 37 out of 40
Submitted Jun 7 at 12:27pm
This attempt took 1,538 minutes.
Question 1
pts
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.
Correct Answer
how well the data fit the assumptions of the model.
how well the regression line fits the observed data.
You Answered
both a and d
how well the data fit the assumptions of the model.
2.8.6
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
1/21
,6/13/25, 9:10 AM Homework 2: Quiz: Regression Analysis - ISYE-6414-OAN/Q2/QIS/ASY
evaluate the performance of how well the predicting variables explain the response under the
linearity assumption.
Question 2
pts
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
Correct!
a., b. and c.
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,
β0, β1 through βp, 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 "
Question 3
pts
If a multiple linear regression model contains 5 quantitative predicting variables and an intercept,
then the number of parameters to estimate is ___.
3
4
5
You Answered
6
Correct Answer
7
2.1. Fundamentals, Objectives and Examples
The model parameters are 0, 1, ..., 5, and . Thus, there are 7 parameters.
2/21
, 6/13/25, 9:10 AM
Question 4
1..5 pts
Select all answers which correspond to common measures of predicting accuracy in multiple
linear regression.
Correct!
mean squared prediction error (MSPE)
Correct!
mean absolute prediction error (MAE)
Correct!
precision measure (PM) or precision error
Correct!
mean absolute percentage error (MAPE)
Correct!
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
Correct!
correlation
3/21
Homework 2: Quiz
Due Jun 8 at 11:59pm
Points 40
Questions 35
Available May 26 at 8am - Jun 8 at 11:59pm
Time Limit None
Instructions
Due: Jun 8 at 11:59 p.m. Eastern
: You only have attempt to answer the questions.
Attention ONE
This quiz was locked Jun 8 at 11:59pm.
Attempt History
Attempt Time
Score for this quiz: 37 out of 40
Submitted Jun 7 at 12:27pm
This attempt took 1,538 minutes.
Question 1
pts
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.
Correct Answer
how well the data fit the assumptions of the model.
how well the regression line fits the observed data.
You Answered
both a and d
how well the data fit the assumptions of the model.
2.8.6
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
1/21
,6/13/25, 9:10 AM Homework 2: Quiz: Regression Analysis - ISYE-6414-OAN/Q2/QIS/ASY
evaluate the performance of how well the predicting variables explain the response under the
linearity assumption.
Question 2
pts
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
Correct!
a., b. and c.
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,
β0, β1 through βp, 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 "
Question 3
pts
If a multiple linear regression model contains 5 quantitative predicting variables and an intercept,
then the number of parameters to estimate is ___.
3
4
5
You Answered
6
Correct Answer
7
2.1. Fundamentals, Objectives and Examples
The model parameters are 0, 1, ..., 5, and . Thus, there are 7 parameters.
2/21
, 6/13/25, 9:10 AM
Question 4
1..5 pts
Select all answers which correspond to common measures of predicting accuracy in multiple
linear regression.
Correct!
mean squared prediction error (MSPE)
Correct!
mean absolute prediction error (MAE)
Correct!
precision measure (PM) or precision error
Correct!
mean absolute percentage error (MAPE)
Correct!
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
Correct!
correlation
3/21
