ISYE 6414 FINAL EXAM REVIEW QUESTIONS WITH
COMPLETE SOLUTIONS GUARANTEED PASS BRAND NEW
2025
Least Square Elimination (LSE) cannot be applied to GLM
models. - 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. - 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. - 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 - 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. - ANSWER
- >True
MLE is used for the GLMs for handling complicated link function
modeling in the X-Y relationship. - ANSWER - >True
,In the GLMs the link function cannot be a non linear regression.
- 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. - 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. - 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. - 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 - ANSWER - >True
When the data may not be normally distributed, AIC is more
appropriate for variable selection than adjusted R-squared -
ANSWER - >True
, The slope of a linear regression equation is an example of a
correlation coefficient. - 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 - 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.
- 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 -
ANSWER - >True
The estimated coefficients of a regression line is positive, when
the coefficient of
determination is positive. - ANSWER - >False - r squared is
always positive.
COMPLETE SOLUTIONS GUARANTEED PASS BRAND NEW
2025
Least Square Elimination (LSE) cannot be applied to GLM
models. - 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. - 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. - 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 - 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. - ANSWER
- >True
MLE is used for the GLMs for handling complicated link function
modeling in the X-Y relationship. - ANSWER - >True
,In the GLMs the link function cannot be a non linear regression.
- 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. - 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. - 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. - 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 - ANSWER - >True
When the data may not be normally distributed, AIC is more
appropriate for variable selection than adjusted R-squared -
ANSWER - >True
, The slope of a linear regression equation is an example of a
correlation coefficient. - 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 - 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.
- 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 -
ANSWER - >True
The estimated coefficients of a regression line is positive, when
the coefficient of
determination is positive. - ANSWER - >False - r squared is
always positive.
