11/12/24, 9:32 AM 2024 newest|ISYE 6414 Final Exam Review|2024-2025 UPDATE|COMPREHENSIVE QUESTIONS AND VERIFIED SOLUTI…
2024 newest|ISYE 6414 Final Exam
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discarded variables have coefficients equal to zero.
Give this one a try later!
Maximum Likelihood Estimation is
Hypothesis testing for Poisson
not applicable for simple linear
1 2 regression can be done on small
regression and multiple linear
sample sizes
regression.
The R-squared and adjusted R-
squared are not appropriate
The test of subset of coefficients
4 model comparisons for non linear
tests the null hypothesis that
regression but are for linear
regression models.
,11/12/24, 9:32 AM 2024 newest|ISYE 6414 Final Exam Review|2024-2025 UPDATE|COMPREHENSIVE QUESTIONS AND VERIFIED SOLUTI…
Terms in this set (111)
Least Square Elimination False - it is applicable but does not use data
(LSE) cannot be applied distribution information fully.
to 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
Estimation is not same 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.
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.
, 11/12/24, 9:32 AM 2024 newest|ISYE 6414 Final Exam Review|2024-2025 UPDATE|COMPREHENSIVE QUESTIONS AND VERIFIED SOLUTI…
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
When the data may not True
be 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.
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