ISYE 6414 FINAL EXAM SCRIPT ACTUAL
EXAM 2026 COMPLETE QUESTIONS AND
ANSWERS GRADED A+
◍ For Poisson regression, we can reduce type I errors of identifying
statistical
significance in the regression coefficients by increasing the sample
size. Answer: True
◍ Both LASSO and ridge regression always provide greater residual
sum of squares
than that of simple multiple linear regression. Answer: True
◍ If data on (Y, X) are available at only two values of X, then the
model Y = \beta_1 X
+ \beta_2 X^2 + \epsilon provides a better fit than Y = \beta_0 +
\beta_1 X +
\epsilon. Answer: False - nothing to determine of a quadratic model is
necessary or required.
◍ If the Cook's distance for any particular observation is greater than
one, that data
point is definitely a record error and thus needs to be discarded.
Answer: False - must see a comparison of data points. Is 1 too large?
, ◍ We can use residual analysis to conclusively determine the
assumption of
independence Answer: False - we can only determine uncorrelated
errors.
◍ It is possible to apply logistic regression when the response
variable Y has 3
classes. Answer: True
◍ . A correlation coefficient close to 1 is evidence of a cause-and-
effect relationship
between the two variables. Answer: False- cause and effect can only
be determined by a well designed experiment.
◍ Multiplying a variable by 10 in LASSO regression, decreases the
chance that the
coefficient of this variable is nonzero. Answer: False - I am not sure
why anyone would think this would be true.
◍ In regression inference, the 99% confidence interval of coefficient
\beta_0 is always
wider than the 95% confidence interval of \beta_1. Answer: False-
can only compare beta1 with beta1 and beta0 with beta0
◍ The regression coefficients for the Poisson regression model can
be estimated in
EXAM 2026 COMPLETE QUESTIONS AND
ANSWERS GRADED A+
◍ For Poisson regression, we can reduce type I errors of identifying
statistical
significance in the regression coefficients by increasing the sample
size. Answer: True
◍ Both LASSO and ridge regression always provide greater residual
sum of squares
than that of simple multiple linear regression. Answer: True
◍ If data on (Y, X) are available at only two values of X, then the
model Y = \beta_1 X
+ \beta_2 X^2 + \epsilon provides a better fit than Y = \beta_0 +
\beta_1 X +
\epsilon. Answer: False - nothing to determine of a quadratic model is
necessary or required.
◍ If the Cook's distance for any particular observation is greater than
one, that data
point is definitely a record error and thus needs to be discarded.
Answer: False - must see a comparison of data points. Is 1 too large?
, ◍ We can use residual analysis to conclusively determine the
assumption of
independence Answer: False - we can only determine uncorrelated
errors.
◍ It is possible to apply logistic regression when the response
variable Y has 3
classes. Answer: True
◍ . A correlation coefficient close to 1 is evidence of a cause-and-
effect relationship
between the two variables. Answer: False- cause and effect can only
be determined by a well designed experiment.
◍ Multiplying a variable by 10 in LASSO regression, decreases the
chance that the
coefficient of this variable is nonzero. Answer: False - I am not sure
why anyone would think this would be true.
◍ In regression inference, the 99% confidence interval of coefficient
\beta_0 is always
wider than the 95% confidence interval of \beta_1. Answer: False-
can only compare beta1 with beta1 and beta0 with beta0
◍ The regression coefficients for the Poisson regression model can
be estimated in
