ISYE 6414 UPDATED MIDTERM QUESTIONS AND
ANSWERS SURE A+
✔✔True - ✔✔in k-fold cross validation, the larger K, the higher the variability in the
estimation of the classification error is.
✔✔1) to model count data
2) to model rate response data
3) to model response data with a Poisson distribution - ✔✔What can Poisson regression
be used for?
✔✔True - ✔✔The link function for the Poisson regression is the log function.
✔✔False - constant variance will be violated. - ✔✔If we apply a standard regression to
response data with a Poisson distribution, constant variance assumption will hold.
✔✔True - ✔✔in Poisson regression, we model the log of the expected response
variable, not the expected log response variable.
✔✔False - fill in later - ✔✔In Poisson regression, we use ordinary least squares to fit the
model.
✔✔True - ✔✔In Poisson regression, we interpret the coefficients in terms of the ratio of
the response rates.
✔✔False - we use z-tests - ✔✔In Poisson regression, we make inference using the t-
intervals for the coefficients.
✔✔False - the estimates for the coefficients are approximate in Poisson regression. -
✔✔in Poisson regression, inference relies on the exact sampling distribution of the
regression coefficients.
, ✔✔True - the test for regression coefficients in Poisson regression follows a chi-square
distribution with q degrees of freedom. - ✔✔We use a chi-square testing procedure to
test whether a subset of regression coefficients are zero in Poisson regression.
✔✔False - fill in later - ✔✔We can use residual analysis in Poisson regression to
evaluate whether errors are uncorrelated.
✔✔1) to address multicollinearity in multiple regression
2) To select among a large number of predicting variables
3) To fit a model when there are more predicting variables than observations - ✔✔What
are some common use cases for variable selection?
✔✔True - ✔✔When selecting variables, it is important to first establish which variables
are used for controlling bias in the sample and which are explanatory.
✔✔True -Variable selection balances bias with variance to select the model. -
✔✔Variable selection methods are performed by balancing the bias-variance tradeoff.
✔✔True - ✔✔The penalty constant Lambda in regularized regression has the role of
controlling the trade off between lack of fit and model complexity.
✔✔True - we can find closed form solutions for the ridge coefficients - ✔✔The ridge
regression coefficients are obtained using an exact or closed form expression.
✔✔True - in Lasso, the coefficient estimates are approximate, we used a numerical
algorithm to estimate them. - ✔✔The estimated coefficients in lasso regression are
obtained using a numerical algorithm.
✔✔True - fill in later - ✔✔The regression coefficients in lasso are less efficient than
those from the ordinary least squares estimation approach.
✔✔True - this is true for explanatory purposes but NOT prediction. - ✔✔When Selecting
variables for explanatory purpose, one might consider including predicting variables
which are correlated if it would help answer your research hypothesis.
✔✔False - Variable selection has come a long way but is far from a solved problem,
especially with many predictors. - ✔✔Variable selection is a simple and solved
statistical problem since we can implement it using software.
✔✔False - it is not good practice to perform variable selection based on the statistical
significance of the coefficients, as significance is almost always derived based on the
other predictors in the model. - ✔✔It is good practice to perform variable selection
based on the statistical significance of the regression coefficients.
ANSWERS SURE A+
✔✔True - ✔✔in k-fold cross validation, the larger K, the higher the variability in the
estimation of the classification error is.
✔✔1) to model count data
2) to model rate response data
3) to model response data with a Poisson distribution - ✔✔What can Poisson regression
be used for?
✔✔True - ✔✔The link function for the Poisson regression is the log function.
✔✔False - constant variance will be violated. - ✔✔If we apply a standard regression to
response data with a Poisson distribution, constant variance assumption will hold.
✔✔True - ✔✔in Poisson regression, we model the log of the expected response
variable, not the expected log response variable.
✔✔False - fill in later - ✔✔In Poisson regression, we use ordinary least squares to fit the
model.
✔✔True - ✔✔In Poisson regression, we interpret the coefficients in terms of the ratio of
the response rates.
✔✔False - we use z-tests - ✔✔In Poisson regression, we make inference using the t-
intervals for the coefficients.
✔✔False - the estimates for the coefficients are approximate in Poisson regression. -
✔✔in Poisson regression, inference relies on the exact sampling distribution of the
regression coefficients.
, ✔✔True - the test for regression coefficients in Poisson regression follows a chi-square
distribution with q degrees of freedom. - ✔✔We use a chi-square testing procedure to
test whether a subset of regression coefficients are zero in Poisson regression.
✔✔False - fill in later - ✔✔We can use residual analysis in Poisson regression to
evaluate whether errors are uncorrelated.
✔✔1) to address multicollinearity in multiple regression
2) To select among a large number of predicting variables
3) To fit a model when there are more predicting variables than observations - ✔✔What
are some common use cases for variable selection?
✔✔True - ✔✔When selecting variables, it is important to first establish which variables
are used for controlling bias in the sample and which are explanatory.
✔✔True -Variable selection balances bias with variance to select the model. -
✔✔Variable selection methods are performed by balancing the bias-variance tradeoff.
✔✔True - ✔✔The penalty constant Lambda in regularized regression has the role of
controlling the trade off between lack of fit and model complexity.
✔✔True - we can find closed form solutions for the ridge coefficients - ✔✔The ridge
regression coefficients are obtained using an exact or closed form expression.
✔✔True - in Lasso, the coefficient estimates are approximate, we used a numerical
algorithm to estimate them. - ✔✔The estimated coefficients in lasso regression are
obtained using a numerical algorithm.
✔✔True - fill in later - ✔✔The regression coefficients in lasso are less efficient than
those from the ordinary least squares estimation approach.
✔✔True - this is true for explanatory purposes but NOT prediction. - ✔✔When Selecting
variables for explanatory purpose, one might consider including predicting variables
which are correlated if it would help answer your research hypothesis.
✔✔False - Variable selection has come a long way but is far from a solved problem,
especially with many predictors. - ✔✔Variable selection is a simple and solved
statistical problem since we can implement it using software.
✔✔False - it is not good practice to perform variable selection based on the statistical
significance of the coefficients, as significance is almost always derived based on the
other predictors in the model. - ✔✔It is good practice to perform variable selection
based on the statistical significance of the regression coefficients.
