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ISYE6414 FINAL EXAM BUNDLE REAL EXAM QUESTION AND CORRECT ANSWERS|ALREADY GRADED A+
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ISYE6414 FINAL EXAM BUNDLE REAL EXAM QUESTION AND CORRECT ANSWERS|ALREADY GRADED A+
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10/29/24, 12:49 AM ISYE6414 FINAL EXAM BUNDLE 2024-2025 REAL EXAM QUESTION AND CORRECT ANSWERS|ALREADY GRADED A+ Fla…ISYE6414 FINAL EXAM BUNDLE 2024-2025 REAL
EXAM QUESTION AND CORRECT
ANSWERS|ALREADY GRADED A+
Terms in this set (111)
Least Square Elimination False - it is applicable but does not use data
(LSE) cannot be applied to distribution information fully.
GLM models.
In multiple linear True - the least squares estimates are BLUE (Best
regression with idd and Linear Unbiased Estimates) in multiple linear
equal variance, the least regression.
squares estimation of
regression coefficients are
always unbiased.
Maximum Likelihood False - In SLR and MLR, the SLE and MLE are the same
Estimation is not with normal idd data.
applicable for simple
linear regression and
multiple linear regression.
The backward elimination False - Type I error
requires a pre-set
probability of type II error
The first degree of True
freedom in the F
distribution for any of the
three procedures in
stepwise is always equal
to one.
https://quizlet.com/963733993/isye6414-final-exam-bundle-2024-2025-real-exam-question-and-correct-answersalready-graded-a-flash-cards/?new 1/17
,10/29/24, 12:49 AM ISYE6414 FINAL EXAM BUNDLE 2024-2025 REAL EXAM QUESTION AND CORRECT ANSWERS|ALREADY GRADED A+ Fla…
MLE is used for the GLMs True
for handling complicated
link function modeling in
the X-Y relationship.
In the GLMs the link False - It can be linear, non linear, or parametric
function cannot be a non
linear regression.
When the p-value of the False - When P value is small, the model fits become
slope estimate in the SLR more significant and R squared become larger.
is small the r-squared
becomes smaller too.
In GLMs the main reason False - The potential constraint in the parameters of
one does not use LSE to GLMs is handled by the link function.
estimate model
parameters is the potential
constrained in the
parameters.
The R-squared and TRUE - The underlying assumption of R-squared
adjusted R-squared are calculations is that you are fitting a linear model.
not appropriate model
comparisons for non
linear regression but are
for linear regression
models.
The decision in using True
ANOVA table for testing
whether a model is
significant depends on the
normal distribution of the
response variable
https://quizlet.com/963733993/isye6414-final-exam-bundle-2024-2025-real-exam-question-and-correct-answersalready-graded-a-flash-cards/?new 2/17
, 10/29/24, 12:49 AM ISYE6414 FINAL EXAM BUNDLE 2024-2025 REAL EXAM QUESTION AND CORRECT ANSWERS|ALREADY GRADED A+ Fla…
When the data may not be True
normally distributed, AIC
is more appropriate for
variable selection than
adjusted R-squared
The slope of a linear False - the correlation coefficient is the r value. Will
regression equation is an have the same + or - sign as the slope.
example of a correlation
coefficient.
In multiple linear False - r squared measures how much variability is
regression, as the value of explained by the model, NOT how strong the
R-squared increases, the predictors are.
relationship
between predictors
becomes stronger
When dealing with a False - the adjusted rsquared value take the number
multiple linear regression and types of predictors into account. It is lower than
model, an adjusted R- the r squared value.
squared can
be greater than the
corresponding unadjusted
R-Squared value.
In a multiple regression True
problem, a quantitative
input variable x is
replaced by x −
mean(x). The R-squared
for the fitted model will be
the same
The estimated coefficients False - r squared is always positive.
of a regression line is
positive, when the
coefficient of
determination is positive.
https://quizlet.com/963733993/isye6414-final-exam-bundle-2024-2025-real-exam-question-and-correct-answersalready-graded-a-flash-cards/?new 3/17
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