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ISYE 6414-REGRESSION ANALYSIS MIDTERM EXAM 500+ QUESTIONS AND DETAILED SOLUTIONS LATEST UPDATE THIS YEAR

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ISYE 6414-REGRESSION ANALYSIS MIDTERM EXAM 500+ QUESTIONS AND DETAILED SOLUTIONS LATEST UPDATE THIS YEAR

Institution
ISYE 6414-REGRESSION ANALYSIS
Course
ISYE 6414-REGRESSION ANALYSIS

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Page 1 of 123



ISYE 6414-REGRESSION ANALYSIS MIDTERM EXAM
500+ QUESTIONS AND DETAILED SOLUTIONS
LATEST UPDATE THIS YEAR
QUESTION: Which one is correct?




a)We can evaluate the goodness of fit a model using the testing procedure of the overall
regression.

b)In applying the deviance test for goodness of fit in logistic regression, we seek large p-values,
that is, not reject the null hypothesis.

c)There is no error term in logistic regression and thus we cannot perform a goodness of fit
assessment.

d)None of the above. - ANSWER-b)In applying the deviance test for goodness of fit in logistic
regression, we seek large p-values, that is, not reject the null hypothesis.




QUESTION: Which is correct?




a)Prediction translates into classification of a future binary response in logistic regression.

b)In order to perform classification in logistic regression, we need to first define a classifier for
the classification error rate.

c)One common approach to estimate the classification error is cross-validation.

d)All of the above. - ANSWER-d)All of the above.

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QUESTION: Comparing cross-validation methods,




a)The random sampling approach is more computational efficient that leave-one-out cross
validation.

b)In K-fold cross-validation, the larger K is, the higher the variability in the estimation of the
classification error is.

c)Leave-one-out cross validation is a particular case of the random sampling cross-validation.

d)None of the above. - ANSWER-b)In K-fold cross-validation, the larger K is, the higher the
variability in the estimation of the classification error is.




QUESTION:

In evaluating a multiple linear model:




a)The F test is used to evaluate the overall regression.

b)The coefficient of variation is interpreted as the percentage of variability in the response
variable explained by the model.

c)Residual analysis is used for goodness of fit assessment.

d)All of the above. - ANSWER-d)All of the above.




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QUESTION: In the presence of near multicollinearity,:




a)The coefficient of variation decreases.

b)The regression coefficients will tend to be identified as statistically significant even if they are
not.

c)The prediction will not be impacted.

d)None of the above. - ANSWER-d)None of the above.




QUESTION: When do we use transformations?




a)If the linearity assumption with respect to one or more predictors does not hold, then we use
transformations of the corresponding predictors to improve on this assumption.

b)If the normality assumption does not hold, we transform the response variable, commonly
using the Box-Cox transformation.

c)If the constant variance assumption does not hold, we transform the response variable.

d)All of the above. - ANSWER-d)All of the above.




QUESTION: Which one is correct?




a)The residuals have constant variance for the multiple linear regression model.


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b)The residuals vs fitted can be used to assess the assumption of independence.

c)The residuals have a t-distribution distribution if the error term is assumed to have a normal
distribution.

d)None of the above. - ANSWER-d)None of the above.




QUESTION: Logistic regression is different from standard linear regression in that




a)It does not have an error term

b)The response variable is not normally distributed.

c)It models probability of a response and not the expectation of the response.

d)All of the above. - ANSWER-d)All of the above.




QUESTION: Which one is correct?




a)The logit link function is the only link function that can be used for modeling binary response
data.

b)Logistic regression models the probability of a success given a set of predicting variables.

c)The interpretation of the regression coefficients in logistic regression is the same as for
standard linear regression assuming normality.




4

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ISYE 6414-REGRESSION ANALYSIS
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ISYE 6414-REGRESSION ANALYSIS

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Number of pages
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Written in
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