ISYE6414 FINAL SCRIPT STUDY GUIDE
COMPLETE QUESTIONS AND ANSWERS
UPDATED ACCURATE GUARANTEED PASS
●● 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.
COMPLETE QUESTIONS AND ANSWERS
UPDATED ACCURATE GUARANTEED PASS
●● 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.
