10/3/24, 5:07 PM ISYE6414 FINAL EXAM 2024-2025 / ISYE6414 FINAL EXAM REAL EXAM QUESTIONS AND 100% CORRECT ANSWERS P…
ISYE6414 FINAL EXAM 2024-2025 / ISYE6414
FINAL EXAM REAL EXAM QUESTIONS AND
100% CORRECT ANSWERS PLUS RATIONALES/
GRADED A
Terms in this set (71)
In Logistic Regression, the True - The relationship that links the predictors is highly
relationship between the non-linear.
probability of success and
the predicting variables is
non-linear.
In Logistic Regression, the False - In logistic regression, there are no error terms.
error terms follow a
normal distribution.
The logit function is the True - the logit function is also known as the log-odds
log of the ratio of the function, which is the ln(P/1-p).
probability of success to
the probability of failure
and is also known as the
log-odds function.
The number of parameters False - As there is no error term in logistic regression,
that need to be estimated there is no additional parameter for the variance of the
in a logistic regression error terms.
model with 6 predicting
variables and an intercept
is the same as the number
of parameters that need to
be estimated in a standard
linear regression model
with an intercept and same
predicting variables.
, 10/3/24, 5:07 PM ISYE6414 FINAL EXAM 2024-2025 / ISYE6414 FINAL EXAM REAL EXAM QUESTIONS AND 100% CORRECT ANSWERS P…
The log-likelihood function False - log-likelihood is a non-linear function, and a
is a linear function with a numerical algorithm is needed in order to maximize it.
closed form solution.
In Logistic Regression, the False - We interpret logistic regression coefficients
estimated value for a with respect to the odds of success.
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.
Under logistic regression, False - The coefficient estimator follows an
the sampling distribution approximate normal distribution.
used for a coefficient
estimator is a chi-square
distribution when the
sample size is large.
When testing a subset of False - when testing a subset of coefficients, deviance
coefficients, deviance follows a chi-square distribution with q degrees of
follows a chi-square freedom, where q is the number of regression
distribution with q degrees coefficients discarded from the full model to get the
of freedom, where q is the reduced model.
number of regression
coefficients in the reduced
model.
Logistic regression deals True - logistic regression is the generalization of the
with the case where the standard regression model that is used when the
dependent variable is response variable y is binary or binomial.
binary and the conditional
distribution is binomial.
It is good practice to False - The residuals can only be defined for logistic
perform a goodness-of-fit regression with replications.
test on logistic regression
models without
replications.
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller MEGAMINDS. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $21.99. You're not tied to anything after your purchase.
