ISYE 6414 Final Exam Review Questions
With Verified Answers
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.
If the outcome variable is quantitative and all explanatory variables take values 0 or
1, a logistic regression model is most appropriate. - answer✔False - More research is necessary
to determine the correct model.
After fitting a logistic regression model, a plot of residuals versus fitted values is
useful for checking if model assumptions are violated. - answer✔False - for logistic regression
use deviance residuals.
In a greenhouse experiment with several predictors, the response variable is the
number of seeds that germinate out of 60 that are planted with different treatment
combinations. A Poisson regression model is most appropriate for modeling this
data - answer✔False - poisson regression models rate or count data.
For Poisson regression, we can reduce type I errors of identifying statistical
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