ISYE 6414 ALL EXAMINERS QUESTIONS AND
ANSWERS SURE A+
✔✔When the objective is to explain the relationship to the response, one might consider
including predicting variables which are correlated - ✔✔True - But this should be
avoided for prediction
✔✔Variable selection addresses multicolinearity, high dimensionaltiy, and prediction vs
explanatory prediction - ✔✔TRUE
✔✔The variables chosen for prediction and the variables chosen for explanatory
objectives will be the same. - ✔✔False
✔✔Variable selection is not special, it is affected by highly correlated variables -
✔✔TRUE
✔✔Confounding variable is a variable that influences both the dependent variable and
independent variable - ✔✔True
✔✔Explanatory variable is one that explains changes in the response variable -
✔✔TRUE
✔✔Predicting variable is used in regression to predict the outcome of another variable. -
✔✔True
✔✔It is good practice apply variable selection without understanding the problem at
hand to reduce bias. - ✔✔False - always understand the problem at hand to better
select variables for the model.
, ✔✔When a statistically insignificant variable is discarded from the model, there is little
change in the other predictors statistical significance. - ✔✔False - it is possible that
when a predictor is discarded, the statistical significance of other variables will change.
✔✔We can do a partial F test to determine if variable selection is necessary. - ✔✔True
✔✔When selecting variables for a model, one needs also to consider the research
hypothesis, as well as any potential confounding variables to control for - ✔✔True
✔✔We would like to have a prediction with low uncertainty for new settings. This means
that we're willing to give up some bias to reduce the variability in the prediction. -
✔✔True
✔✔Generally models with covariance have high bias but low variance - ✔✔False - they
have low bias but high variance.
✔✔A measure of the bias-variance tradeoff is the prediction risk - ✔✔TRUE
✔✔To estimate prediction risk we compute the prediction risk for the observed data and
take the sum of squared differences between fitted values for sub model S and the
observed values. - ✔✔True - this is called training risk and it is a biased estimate of
prediction risk
✔✔The larger the number of variables in the model, the larger the training risk. -
✔✔False - the larger the number of variables in a model the lower the training risk.
✔✔The Mallow's CP complexity penalty is two times the size of the model (the number
of variables in the submodel) times the estimated variance divided by n. - ✔✔True
✔✔AIC looks just like the Mallow's Cp except that the variance is the true variance and
not its estimate. - ✔✔True
✔✔Another criteria for variable selection is cross validation which is a direct measure of
explanatory power. - ✔✔False - Predictive power
✔✔Stepwise is a heuristic search - ✔✔TRUE it is also a greedy search that does not
guarantee to find the best score
✔✔If p is larger than n, stepwise is feasible - ✔✔TRUE - for forward, but not backward
✔✔Forward stepwise will select larger models than backward. - ✔✔False - it will
typically select smaller models especially if p is large
ANSWERS SURE A+
✔✔When the objective is to explain the relationship to the response, one might consider
including predicting variables which are correlated - ✔✔True - But this should be
avoided for prediction
✔✔Variable selection addresses multicolinearity, high dimensionaltiy, and prediction vs
explanatory prediction - ✔✔TRUE
✔✔The variables chosen for prediction and the variables chosen for explanatory
objectives will be the same. - ✔✔False
✔✔Variable selection is not special, it is affected by highly correlated variables -
✔✔TRUE
✔✔Confounding variable is a variable that influences both the dependent variable and
independent variable - ✔✔True
✔✔Explanatory variable is one that explains changes in the response variable -
✔✔TRUE
✔✔Predicting variable is used in regression to predict the outcome of another variable. -
✔✔True
✔✔It is good practice apply variable selection without understanding the problem at
hand to reduce bias. - ✔✔False - always understand the problem at hand to better
select variables for the model.
, ✔✔When a statistically insignificant variable is discarded from the model, there is little
change in the other predictors statistical significance. - ✔✔False - it is possible that
when a predictor is discarded, the statistical significance of other variables will change.
✔✔We can do a partial F test to determine if variable selection is necessary. - ✔✔True
✔✔When selecting variables for a model, one needs also to consider the research
hypothesis, as well as any potential confounding variables to control for - ✔✔True
✔✔We would like to have a prediction with low uncertainty for new settings. This means
that we're willing to give up some bias to reduce the variability in the prediction. -
✔✔True
✔✔Generally models with covariance have high bias but low variance - ✔✔False - they
have low bias but high variance.
✔✔A measure of the bias-variance tradeoff is the prediction risk - ✔✔TRUE
✔✔To estimate prediction risk we compute the prediction risk for the observed data and
take the sum of squared differences between fitted values for sub model S and the
observed values. - ✔✔True - this is called training risk and it is a biased estimate of
prediction risk
✔✔The larger the number of variables in the model, the larger the training risk. -
✔✔False - the larger the number of variables in a model the lower the training risk.
✔✔The Mallow's CP complexity penalty is two times the size of the model (the number
of variables in the submodel) times the estimated variance divided by n. - ✔✔True
✔✔AIC looks just like the Mallow's Cp except that the variance is the true variance and
not its estimate. - ✔✔True
✔✔Another criteria for variable selection is cross validation which is a direct measure of
explanatory power. - ✔✔False - Predictive power
✔✔Stepwise is a heuristic search - ✔✔TRUE it is also a greedy search that does not
guarantee to find the best score
✔✔If p is larger than n, stepwise is feasible - ✔✔TRUE - for forward, but not backward
✔✔Forward stepwise will select larger models than backward. - ✔✔False - it will
typically select smaller models especially if p is large
