ISYE 6414 Final Exam Review
We cannot use the training error rate as an estimate of the true error classification error
rate because it is biased upward. - CORRECT ANSWER-False - biased downward
Random sampling is computationally more expensive than the K-fold cross validation,
with no clear advantage in terms of the accuracy of the estimation classification error
rate. - CORRECT ANSWER-True
Leave on out cross validation is preferred - CORRECT ANSWER-False - K fold is
preferred.
The larger K is, the larger the number of folds, the less bias the estimate of the
classification the error is but has higher variability. - CORRECT ANSWER-True
In Poisson regression underlying assumption is that the response variable has a
Poisson distribution, or responses could be wait times, or exponential distribution -
CORRECT ANSWER-True
The g link function is also called the canonical link function. - CORRECT ANSWER-
True - which means that parameter estimates under logistic regression are fully efficient
and tests on those parameters are better behaved for small samples.
Poisson distribution, the variance is equal to the expectation. Thus, the variance is not
constant - CORRECT ANSWER-True
For Poisson regression we estimate the expectation of the log response variable. -
CORRECT ANSWER-False - we estimate the log of the expectation of the response
variable.
Standard linear regression could be used to model Poisson regression using the
variance stabilizing transformation sqrt(mu-3/8) if the number of counts is large -
CORRECT ANSWER-True - the number of counts can be small - then use Poisson
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.
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
We cannot use the training error rate as an estimate of the true error classification error
rate because it is biased upward. - CORRECT ANSWER-False - biased downward
Random sampling is computationally more expensive than the K-fold cross validation,
with no clear advantage in terms of the accuracy of the estimation classification error
rate. - CORRECT ANSWER-True
Leave on out cross validation is preferred - CORRECT ANSWER-False - K fold is
preferred.
The larger K is, the larger the number of folds, the less bias the estimate of the
classification the error is but has higher variability. - CORRECT ANSWER-True
In Poisson regression underlying assumption is that the response variable has a
Poisson distribution, or responses could be wait times, or exponential distribution -
CORRECT ANSWER-True
The g link function is also called the canonical link function. - CORRECT ANSWER-
True - which means that parameter estimates under logistic regression are fully efficient
and tests on those parameters are better behaved for small samples.
Poisson distribution, the variance is equal to the expectation. Thus, the variance is not
constant - CORRECT ANSWER-True
For Poisson regression we estimate the expectation of the log response variable. -
CORRECT ANSWER-False - we estimate the log of the expectation of the response
variable.
Standard linear regression could be used to model Poisson regression using the
variance stabilizing transformation sqrt(mu-3/8) if the number of counts is large -
CORRECT ANSWER-True - the number of counts can be small - then use Poisson
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.
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
