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ISYE 6414 FINAL EXAM AND PRACTICE EXAM NEWEST ACTUAL EXAM COMPLETE 300 QUESTIONS AND CORRECT DETAILED ANSWERS (VERIFIED ANSWERS) |ALREADY GRADED A+$27.99
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ISYE 6414 FINAL EXAM AND PRACTICE EXAM NEWEST ACTUAL EXAM COMPLETE 300 QUESTIONS AND CORRECT DETAILED ANSWERS (VERIFIED ANSWERS) |ALREADY GRADED A+
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Course
ISYE 6414
Institution
ISYE 6414
ISYE 6414 FINAL EXAM AND PRACTICE EXAM
NEWEST ACTUAL EXAM COMPLETE 300
QUESTIONS AND CORRECT DETAILED ANSWERS
(VERIFIED ANSWERS) |ALREADY GRADED A+
True - The relationship that links the predictors is highly
non-linear. - CORRECT ANSWER In Logistic Regression, the
relationship between th...
ISYE 6414 FINAL EXAM AND PRACTICE EXAM
NEWEST ACTUAL EXAM COMPLETE 300
QUESTIONS AND CORRECT DETAILED ANSWERS
(VERIFIED ANSWERS) |ALREADY GRADED A+
True - The relationship that links the predictors is highly
non-linear. - CORRECT ANSWER In Logistic Regression, the
relationship between the probability of success and the
predicting variables is non-linear.
False - In logistic regression, there are no error terms. -
CORRECT ANSWER In Logistic Regression, the error terms
follow a normal distribution.
True - the logit function is also known as the log-odds
function, which is the ln(P/1-p). - CORRECT ANSWER The logit
function is the log of the ratio of the probability of success
to the probability of failure and is also known as the log-
odds function.
False - As there is no error term in logistic regression, there
is no additional parameter for the variance of the error
terms. - CORRECT ANSWER The number of parameters that
need to be estimated in a logistic regression 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.
,False - log-likelihood is a non-linear function, and a
numerical algorithm is needed in order to maximize it. -
CORRECT ANSWER The log-likelihood function is a linear
function with a closed form solution.
False - We interpret logistic regression coefficients with
respect to the odds of success. - CORRECT 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 - The coefficient estimator follows an approximate
normal distribution. - CORRECT ANSWER Under logistic
regression, the sampling distribution used for a coefficient
estimator is a chi-square distribution when the sample size
is large.
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. - CORRECT
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.
True - logistic regression is the generalization of the
standard regression model that is used when the response
variable y is binary or binomial. - CORRECT ANSWER Logistic
regression deals with the case where the dependent
,variable is binary and the conditional distribution is
binomial.
False - The residuals can only be defined for logistic
regression with replications. - CORRECT ANSWER It is good
practice to perform a goodness-of-fit test on logistic
regression models without replications.
False - for logistic regression, if the p-value of the deviance
test for GOD is large, then the model is a good fit. - CORRECT
ANSWER In Logistic regression, if the p-value of the
deviance test for GOF is smaller than the significance level
alpha, then is is plausible that the model is a good fit.
False - GOF is no guarantee for good prediction and vice-
versa. - CORRECT ANSWER If a logistic regression model
provides accurate classification, then we can conclude that
it is a good fir for the data.
True - the deviance residuals are approximately N(0,1) if the
model is a good fit to the data. - CORRECT ANSWER For both
logistic regression and Poisson regression, the deviance
residuals should follow an approximate standard normal
distribution if the model is a good fit for the data.
False - CORRECT 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.
True - we can use the Pearson or deviance residuals, but
only if the model has replications. - CORRECT ANSWER
Although there are no error terms in logistic regression
, model using binary data with replications, we can still
perform residual analysis.
True - The error rate is biased downwards, since the model
sees the data 2 times, once for training and once for testing.
- CORRECT ANSWER For a classification model, the training
error tends to underestimate the true classification error
rate of the model.
True - the parameters and their standard errors are
approximate. - CORRECT ANSWER The estimated regression
coefficients in Poisson regression are approximate.
False - we use a z-test, since the the distributions are
approximately normal with large N. - CORRECT ANSWER A t-
test is used for testing the statistical significance of a
coefficient given all predicting variables in a Poisson
regression model.
True - CORRECT ANSWER An overdispersion parameter of 1
indicates that the variability of the response is close to the
variability estimated by the model.
False - we assume that the log rate is a linear combination
of the predicting variables, hence Poisson regression is a
generalized linear model (GLM) - CORRECT ANSWER In
Poisson regression, we assume a non linear relationship
between the log rate and the predicting variables.
True - CORRECT ANSWER Logistic regression models the
probability of a success given a set of predicting variables.
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