Logistic regression is different from standard linear regression in that: correct answers It does not have an error term; The response variable is not normally distributed; It models probability of a response and not the expectation of the response
Logistic regression models correct answers The ...
logistic regression is different from standard lin
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Logistic regression is different from standard linear regression in that: correct answers It does not
have an error term; The response variable is not normally distributed; It models probability of a
response and not the expectation of the response
Logistic regression models correct answers The probability of a success given a set of predicting
variables
In logistic regression correct answers The estimation of the regression coefficients is based on
maximum likelihood estimation
Using the R statistical software to fit a logistic regression, correct answers We can obtain both
the estimates and the standard deviations of the estimates for the regression coefficients
Logistic regression is different from standard linear regression in that correct answers The
sampling distribution of the regression coefficient is approximate; A large sample data is
required for making accurate statistical inferences; A normal sampling distribution is used
instead of a t-distribution for statistical inference.
In logistic regression, correct answers The hypothesis test for subsets of coefficients is
approximate, it relies on a large sample size and is Chi-square
In logistic regression: correct answers The sampling distribution of the residual is approximately
normal distribution if the model is a good fit.
True or False? In applying the deviance test for goodness of fit in logistic regression, we seek
large p-values, that is, not reject the null hypothesis. correct answers True
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 evaluate the classification error is cross-validation.
D) All of the above correct answers D) All of the above
Comparing cross-validation methods, correct answers In K-fold cross-validation, the larger K is,
the higher the variability in the estimation of the classification error is.
Poisson regression can be used: correct answers To model count data.
To model rate response data.
To model response data with a Poisson distribution.
Which one is correct?
, a)The standard normal regression, the logistic regression and the Poisson regression are all
falling under the generalized linear model framework.
b) If we were to apply a standard normal regression to response data with a Poisson distribution,
the constant variance assumption would not hold.
c) The link function for the Poisson regression is the log function.
d) All of the above correct answers d) All of the above
In Poisson regression: correct answers We model the log of the expected response variable not
the expected log response variable.
Which one is correct?
A) The estimated regression coefficients and their standard deviations are approximate not exact
in Poisson regression.
B) We use the glm() R command to fit a Poisson linear regression.
C) The interpretation of the estimated regression coefficients is in terms of the ratio of the
response rates.
D) All of the above correct answers D) All of the above
In Poission regression correct answers We make inference using z-intervals for the regression
coefficients; Statistical inference relies on approximate sampling; Statistical inference is not
reliable for small sample data
True or False? We use a chi-square testing procedure to test whether a subset of regression
coefficients are zero in Poisson regression. correct answers True
Residual analysis in Poisson regression can be used: correct answers To evaluate goodness of fit
of the model
When we do not have a good fit in generalized linear models, it may be that: correct answers We
need to transform some of the predicting variables or to include other variables; The variability
of the expected rate is higher than estimated; There may be leverage points that need to be
explored further.
True or False: In logistic regression, the relationship between the probability of success and the
predicting variables is nonlinear. correct answers True
True or False: In logistic regression, the error terms are assumed to follow a normal distribution.
correct answers False. There are no error terms in logistic regression.
True or False: The logit function is the log of the ratio of the probability of success to the
probability of failure. It is also known as the log odds function. correct answers True
True or False: 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. correct answers False
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