ISYE 6414 Final Exam Review Questions And Answers 100% Verified.
7 views 0 purchase
Module
ISYE 6414
Institution
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
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller techgrades. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for £10.61. You're not tied to anything after your purchase.
