ISYE 6414 Final Exam Review Questions With Correct Verified Answers A+ Graded
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Course
ISYE 6414
Institution
ISYE 6414
Least Square Elimination (LSE) cannot be applied to GLM models. - ANS False - it is applicable but does not use data distribution information fully.
In multiple linear regression with idd and equal variance, the least squares estimation of regression coefficients are always unbiased. - ANS T...
, Least Square Elimination (LSE) cannot be applied to GLM models. -
ANS False - it is applicable but does not use data distribution
information fully.
In multiple linear regression with idd and equal variance, the least
squares estimation of regression coefficients are always unbiased. -
K
ANS True - the least squares estimates are BLUE (Best Linear
Unbiased Estimates) in multiple linear regression.
C
Maximum Likelihood Estimation is not applicable for simple linear
LO
regression and multiple linear regression. - ANS False - In SLR and
MLR, the SLE and MLE are the same with normal idd data.
The backward elimination requires a pre-set probability of type II error
YC
- ANS False - Type I error
The first degree of freedom in the F distribution for any of the three
procedures in stepwise is always equal to one. - ANS True
D
MLE is used for the GLMs for handling complicated link function
U
modeling in the X-Y relationship. - ANS True
ST
In the GLMs the link function cannot be a non linear regression. -
ANS False - It can be linear, non linear, or parametric
When the p-value of the slope estimate in the SLR is small the
r-squared becomes smaller too. - ANS False - When P value is
small, the model fits become more significant and R squared become
larger.
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