1 | Page
ISYE 6402 MIDTERM PREP 2025 UPDATED
ACTUAL EXAM WITH CORRECT SOLUTIONS.
We can assess the constant variance assumption in linear
regression by plotting the residuals vs. fitted values. - correct
answer- True
If one confidence interval in the pairwise comparison in ANOVA
includes zero, we conclude that the two corresponding means
are plausibly equal. - correct answer- True
The assumption of normality is not required in linear regression
to make inference on the regression coefficients. - correct
answer- False (Explanation: is required)
We cannot estimate a multiple linear regression model if the
predicting variables are linearly independent. - correct answer-
False (Explanation: linearly dependent)
If a predicting variable is a categorical variable with 5
categories in a linear regression model without intercept, we
will include 5 dummy variables. - correct answer- True
, 2 | Page
If the normality assumption does not hold for a regression, we
may use a transformation on the response variable. - correct
answer- True
The prediction of the response variable has higher uncertainty
than the estimation of the mean response. - correct answer-
True
Statistical inference for linear regression under normality relies
on large sample size. - correct answer- False (Explanation:
small sample size is fine)
A nonlinear relationship between the response variable and a
predicting variable cannot be modeled using regression. -
correct answer- False (Explanation: Nonlinear relationships can
often be modeled using linear regression by including
polynomial terms of the predicting variable, for example.)
Assumption of normality in linear regression is required for
confidence intervals, prediction intervals, and hypothesis
testing. - correct answer- True
ISYE 6402 MIDTERM PREP 2025 UPDATED
ACTUAL EXAM WITH CORRECT SOLUTIONS.
We can assess the constant variance assumption in linear
regression by plotting the residuals vs. fitted values. - correct
answer- True
If one confidence interval in the pairwise comparison in ANOVA
includes zero, we conclude that the two corresponding means
are plausibly equal. - correct answer- True
The assumption of normality is not required in linear regression
to make inference on the regression coefficients. - correct
answer- False (Explanation: is required)
We cannot estimate a multiple linear regression model if the
predicting variables are linearly independent. - correct answer-
False (Explanation: linearly dependent)
If a predicting variable is a categorical variable with 5
categories in a linear regression model without intercept, we
will include 5 dummy variables. - correct answer- True
, 2 | Page
If the normality assumption does not hold for a regression, we
may use a transformation on the response variable. - correct
answer- True
The prediction of the response variable has higher uncertainty
than the estimation of the mean response. - correct answer-
True
Statistical inference for linear regression under normality relies
on large sample size. - correct answer- False (Explanation:
small sample size is fine)
A nonlinear relationship between the response variable and a
predicting variable cannot be modeled using regression. -
correct answer- False (Explanation: Nonlinear relationships can
often be modeled using linear regression by including
polynomial terms of the predicting variable, for example.)
Assumption of normality in linear regression is required for
confidence intervals, prediction intervals, and hypothesis
testing. - correct answer- True
