ISYE 6414-REGRESSION ANALYSIS
PROJECT REPORT CERTIFICATION SCRIPT
2026 QUESTIONS WITH SOLUTIONS
GRADED A+
◍ 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.
Answer: What are some common use cases for variable selection?
◍ How do we compute classification error? (2 ways).
Answer: 1. Training error 2. Cross validation
◍ False - We interpret logistic regression coefficients with respect to the odds
of success..
Answer: In Logistic Regression, the estimated value for a regression
coefficient B represents the estimated expected change in the response
variable associated with a one unit increase in the predicting variable,
holding all else fixed.
◍ False - GOF is no guarantee for good prediction and vice-versa..
Answer: If a logistic regression model provides accurate classification, then
we can conclude that it is a good fir for the data.
◍ T/F: An approximate test can be used to test for the overall regression in
Poisson regression..
Answer: T
◍ True - The relationship that links the predictors is highly non-linear..
Answer: In Logistic Regression, the relationship between the probability of
success and the predicting variables is non-linear.
,◍ False - fill in later.
Answer: In Poisson regression, we use ordinary least squares to fit the
model.
◍ 1) The sampling distribution of the regression coefficients is approximate 2)
a large sample size is required for making accurate statistical inferences 3) a
normal sampling distribution is used instead of a t-distribution for statistical
inference.
Answer: Differences between logistic regression and linear regression -
statistical inference.
◍ marginal relationship.
Answer: Capturing the association of a predicting variable to the response
variable marginally, i.e. without consideration of other factors.
◍ T/F: The statistical inference for logistic regression relies on large size of
the sample data..
Answer: T
◍ False - when testing a subset of coefficients, deviance follows a chi-square
distribution with q degrees of freedom, where q is the number of regression
coefficients discarded from the full model to get the reduced model..
Answer: When testing a subset of coefficients, deviance follows a chi-square
distribution with q degrees of freedom, where q is the number of regression
coefficients in the reduced model.
◍ T/F: The error term in logistic regression has a normal distribution..
Answer: F
◍ Does the statistical inference for logistic regression rely on a small or large
sample size?.
Answer: Large, if it was a small then the statistical inference is not reliable
◍ 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.D) None of the
above..
Answer: B
◍ False.
Answer: The logit link function is the best link function to model binary
response data because it always fits the data better than other link functions.
◍ Residual analysis in Poisson regression can be used:A) To evaluate
goodness of fit of the model.B) To evaluate whether the relationship
between the log of the expected response and the predicting variables is
linear.C) To evaluate whether the data are uncorrelated.D) All of the above..
Answer: D
◍ Poisson regression.
Answer: commonly used for modeling count or rate data.
◍ False - fill in later.
Answer: The estimations of the regression coefficients is based on
minimizing the sum of least squares in logistic regression.
◍ In logistic regression, we model the__________________, not the response
variable, given the predicting variables..
Answer: probability of a success
◍ True - the parameters and their standard errors are approximate..
Answer: The estimated regression coefficients in Poisson regression are
approximate.
◍ For Poisson regression, the variance = ?.
Answer: rate lambda
◍ True.
Answer: In Poisson regression, we interpret the coefficients in terms of the
ratio of the response rates.
◍ True.
PROJECT REPORT CERTIFICATION SCRIPT
2026 QUESTIONS WITH SOLUTIONS
GRADED A+
◍ 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.
Answer: What are some common use cases for variable selection?
◍ How do we compute classification error? (2 ways).
Answer: 1. Training error 2. Cross validation
◍ False - We interpret logistic regression coefficients with respect to the odds
of success..
Answer: In Logistic Regression, the estimated value for a regression
coefficient B represents the estimated expected change in the response
variable associated with a one unit increase in the predicting variable,
holding all else fixed.
◍ False - GOF is no guarantee for good prediction and vice-versa..
Answer: If a logistic regression model provides accurate classification, then
we can conclude that it is a good fir for the data.
◍ T/F: An approximate test can be used to test for the overall regression in
Poisson regression..
Answer: T
◍ True - The relationship that links the predictors is highly non-linear..
Answer: In Logistic Regression, the relationship between the probability of
success and the predicting variables is non-linear.
,◍ False - fill in later.
Answer: In Poisson regression, we use ordinary least squares to fit the
model.
◍ 1) The sampling distribution of the regression coefficients is approximate 2)
a large sample size is required for making accurate statistical inferences 3) a
normal sampling distribution is used instead of a t-distribution for statistical
inference.
Answer: Differences between logistic regression and linear regression -
statistical inference.
◍ marginal relationship.
Answer: Capturing the association of a predicting variable to the response
variable marginally, i.e. without consideration of other factors.
◍ T/F: The statistical inference for logistic regression relies on large size of
the sample data..
Answer: T
◍ False - when testing a subset of coefficients, deviance follows a chi-square
distribution with q degrees of freedom, where q is the number of regression
coefficients discarded from the full model to get the reduced model..
Answer: When testing a subset of coefficients, deviance follows a chi-square
distribution with q degrees of freedom, where q is the number of regression
coefficients in the reduced model.
◍ T/F: The error term in logistic regression has a normal distribution..
Answer: F
◍ Does the statistical inference for logistic regression rely on a small or large
sample size?.
Answer: Large, if it was a small then the statistical inference is not reliable
◍ 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.D) None of the
above..
Answer: B
◍ False.
Answer: The logit link function is the best link function to model binary
response data because it always fits the data better than other link functions.
◍ Residual analysis in Poisson regression can be used:A) To evaluate
goodness of fit of the model.B) To evaluate whether the relationship
between the log of the expected response and the predicting variables is
linear.C) To evaluate whether the data are uncorrelated.D) All of the above..
Answer: D
◍ Poisson regression.
Answer: commonly used for modeling count or rate data.
◍ False - fill in later.
Answer: The estimations of the regression coefficients is based on
minimizing the sum of least squares in logistic regression.
◍ In logistic regression, we model the__________________, not the response
variable, given the predicting variables..
Answer: probability of a success
◍ True - the parameters and their standard errors are approximate..
Answer: The estimated regression coefficients in Poisson regression are
approximate.
◍ For Poisson regression, the variance = ?.
Answer: rate lambda
◍ True.
Answer: In Poisson regression, we interpret the coefficients in terms of the
ratio of the response rates.
◍ True.
