ISYE 6414 Final Exam Review Questions And Answers 100% Verified.
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ISYE 6414
ISYE 6414 Final Exam Review Questions And Answers 100% Verified.
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,...
ISYE 6414 Final Exam Review Questions
And Answers 100% Verified.
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.
If the outcome variable is quantitative and all explanatory variables take values 0 or
1, a logistic regression model is most appropriate. - correct 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. - correct answer. False - for
logistic regression use deviance residuals.
In a greenhouse experiment with several predictors, the response variable is the
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