ISYE 6414 FINAL EXAM 2024’25 |ACCURATE ANSWERS |VERIFIED
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ISYE 6414
ISYE 6414 FINAL EXAM 2024’25 |ACCURATE ANSWERS |VERIFIED
Least Square Elimination (LSE) cannot be applied to GLM models. - ACCURATE ANSWERFalse - it is applicable but does not use data distribution information fully.
In multiple linear regression with idd and equal variance, the least squar...
ISYE 6414 FINAL EXAM 2024’25 |
ACCURATE ANSWERS |VERIFIED
Least Square Elimination (LSE) cannot be applied to GLM models. - ACCURATE
ANSWER✅✅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. - ACCURATE
ANSWER✅✅True - the least squares estimates are BLUE (Best Linear Unbiased
Estimates) in multiple linear regression.
Maximum Likelihood Estimation is not applicable for simple linear regression and
multiple linear regression. - ACCURATE ANSWER✅✅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 -
ACCURATE ANSWER✅✅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. - ACCURATE ANSWER✅✅True
MLE is used for the GLMs for handling complicated link function modeling in the
X-Y relationship. - ACCURATE ANSWER✅✅True
In the GLMs the link function cannot be a non linear regression. - ACCURATE
ANSWER✅✅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. - ACCURATE ANSWER✅✅False - When P value is small, the
model fits become more significant and R squared become larger.
In GLMs the main reason one does not use LSE to estimate model parameters is
the potential constrained in the parameters. - ACCURATE ANSWER✅✅False -
The potential constraint in the parameters of GLMs is handled by the link function.
The R-squared and adjusted R-squared are not appropriate model comparisons for
non linear regression but are for linear regression models. - ACCURATE
ANSWER✅✅TRUE - The underlying assumption of R-squared calculations is
that you are fitting a linear model.
The decision in using ANOVA table for testing whether a model is significant
depends on the normal distribution of the response variable - ACCURATE
ANSWER✅✅True
When the data may not be normally distributed, AIC is more appropriate for
variable selection than adjusted R-squared - ACCURATE ANSWER✅✅True
The slope of a linear regression equation is an example of a correlation coefficient.
- ACCURATE ANSWER✅✅False - the correlation coefficient is the r value. Will
have the same + or - sign as the slope.
In multiple linear regression, as the value of R-squared increases, the relationship
between predictors becomes stronger - ACCURATE ANSWER✅✅False - r
squared measures how much variability is explained by the model, NOT how
strong the predictors are.
When dealing with a multiple linear regression model, an adjusted R-squared can
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