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ISYE 6414 Final Exam Review
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ISYE 6414 Final Exam Review
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ISYE 6414 Final Exam Review1. Least Square Elimination (LSE) cannot be applied to GLM models.: False
- it is applicable but does not use data distribution information fully.
2. In multiple linear regression with idd and equal variance, the least
squares estimation of regression coefficients are always unbiased.: True -
the least squares estimates are BLUE (Best Linear Unbiased Estimates)
in multiple linear regression.
3. Maximum Likelihood Estimation is not applicable for simple linear regres-
sion and multiple linear regression.: False - In SLR and MLR, the SLE and
MLE are the same with normal idd data.
4.The backward elimination requires a pre-set probability of type II error:
False
- Type I error
5. The first degree of freedom in the F distribution for any of the three
proce- dures in stepwise is always equal to one.: True
6. MLE is used for the GLMs for handling complicated link function
modeling in the X-Y relationship.: True
7. In the GLMs the link function cannot be a non linear regression.: False -
1
,It can be linear, non linear, or parametric
8. When the p-value of the slope estimate in the SLR is small the r-squared
becomes smaller too.: False - When P value is small, the model fits
become more significant and R squared become larger.
9. In GLMs the main reason one does not use LSE to estimate model para-
meters is the potential constrained in the parameters.: False - The
potential constraint in the parameters of GLMs is handled by the link
function.
10.The R-squared and adjusted R-squared are not appropriate model
compar- isons for non linear regression but are for linear regression
models.: TRUE - The underlying assumption of R-squared calculations is
that you are fitting a linear model.
11.The decision in using ANOVA table for testing whether a model is
signifi- cant depends on the normal distribution of the response variable:
True
12.When the data may not be normally distributed, AIC is more appropriate
for variable selection than adjusted R-squared: True
13.The slope of a linear regression equation is an example of a correlation
coefficient.: False - the correlation coefficient is the r value. Will have
2
, the same + or - sign as the slope.
14. In multiple linear regression, as the value of R-squared increases,
the relationship
between predictors becomes stronger: False - r squared measures how
much variability is explained by the model, NOT how strong the
predictors are.
15. When dealing with a multiple linear regression model, an
adjusted R-squared can
be greater than the corresponding unadjusted R-Squared value.: False - the
adjusted rsquared value take the number and types of predictors into
account. It is lower than the r squared value.
16. 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: True
17. The estimated coefficients of a regression line is positive, when the
coef- ficient of
determination is positive.: False - r squared is always positive.
18. If the outcome variable is quantitative and all explanatory variables
take values 0 or
3
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