ISYE6414 FINAL EXAM / ISYE6414 FINAL EXAM REAL EXAM QUESTIONS AND 100% CORRECT ANSWERS PLUS RATIONALES/ GRADED A
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ISYE6414 FINA
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ISYE6414 FINA
ISYE6414 FINAL EXAM / ISYE6414 FINAL
EXAM REAL EXAM QUESTIONS AND 100% CORRECT
ANSWERS PLUS RATIONALES/ GRADED A
ISYE6414 FINAL EXAM / ISYE6414 FINAL
EXAM REAL EXAM QUESTIONS AND 100% CORRECT
ANSWERS PLUS RATIONALES/ GRADED A
ISYE6414 FINAL EXAM 2023-2024 / ISYE6414 FINAL
EXAM REAL EXAM QUESTIONS AND 100% CORRECT
ANSWERS PLUS RATIONALES/ GRADED A
Least Square Elimination (LSE) cannot be applied to GLM models. - CORRECT 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. - CORRECT 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. - CORRECT 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 - CORRECT 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. - CORRECT ANSWER-True
MLE is used for the GLMs for handling complicated link function modeling in the X-Y relationship. -
CORRECT ANSWER-True
In the GLMs the link function cannot be a non linear regression. - CORRECT 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. -
CORRECT 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. - CORRECT 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. - CORRECT 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 - CORRECT ANSWER-True
When the data may not be normally distributed, AIC is more appropriate for variable selection than
adjusted R-squared - CORRECT ANSWER-True
The slope of a linear regression equation is an example of a correlation coefficient. - CORRECT 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 - CORRECT 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
be greater than the corresponding unadjusted R-Squared value. - CORRECT ANSWER-False - the adjusted
rsquared value take the number and types of predictors into account. It is lower than the r squared
value.
In a multiple regression problem, a quantitative input variable x is replaced by x −
mean(x). The R-squared for the fitted model will be the same - CORRECT ANSWER-True
The estimated coefficients of a regression line is positive, when the coefficient of
determination is positive. - CORRECT ANSWER-False - r squared is always positive.
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