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2024 newest|ISYE 6414 Final Exam Review| UPDATE|COMPREHENSIVE QUESTIONS AND VERIFIED SOLUTIONS/CORRECT ANSWERS|GET 100% ACCURATE!! A correlation coefficient close to 1 is evidence of a cause-and-effect relationship between the two variables. - ANSWER>&
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2024 newest|ISYE 6414 Final Exam Review| UPDATE|COMPREHENSIVE
QUESTIONS AND VERIFIED SOLUTIONS/CORRECT ANSWERS|GET 100%
ACCURATE!!
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
...
. 2024 newest|ISYE 6414 Final Exam Review| UPDATE|COMPREHENSIVE
QUESTIONS AND VERIFIED SOLUTIONS/CORRECT ANSWERS|GET 100%
ACCURATE!!
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.
A measure of the bias-variance tradeoff is the prediction risk - ✔✔ANSWER✔✔>>TRUE
After fitting a logistic regression model, a plot of residuals versus fitted values is
useful for checking if model assumptions are violated. - ✔✔ANSWER✔✔>>False - for
logistic regression use deviance residuals.
AIC looks just like the Mallow's Cp except that the variance is the true variance and not
its estimate. - ✔✔ANSWER✔✔>>True
An indication that a higher order non linear relationship better fits the data is that the
dummy variables are all, or nearly all, statistically significant - ✔✔ANSWER✔✔>>True
Another criteria for variable selection is cross validation which is a direct measure of
explanatory power. - ✔✔ANSWER✔✔>>False - Predictive power
Both LASSO and ridge regression always provide greater residual sum of squares
than that of simple multiple linear regression. - ✔✔ANSWER✔✔>>True
Classification is nothing else than prediction of binary responses. -
✔✔ANSWER✔✔>>True
Confounding variable is a variable that influences both the dependent variable and
independent variable - ✔✔ANSWER✔✔>>True
Event rates can be calculated as events per units of varying size, this unit of size is
called exposure - ✔✔ANSWER✔✔>>True
Explanatory variable is one that explains changes in the response variable -
✔✔ANSWER✔✔>>TRUE
For large sample size data, the distribution of the test statistic, assuming the null
hypothesis, is a chi-squared distribution - ✔✔ANSWER✔✔>>True
, For logistic regression we can define residuals for evaluating model goodness of fit for
models with and without replication. - ✔✔ANSWER✔✔>>False - can only be with
replication under the assumption that Yi is binary and n1 is greater than 1
For Poisson regression we estimate the expectation of the log response variable. -
✔✔ANSWER✔✔>>False - we estimate the log of the expectation of the response
variable.
For Poisson regression, we can reduce type I errors of identifying statistical
significance in the regression coefficients by increasing the sample size. -
✔✔ANSWER✔✔>>True
For the testing procedure for subsets of coefficients, we compare the likelihood of a
reduced model versus a full model. This is a goodness of fit test -
✔✔ANSWER✔✔>>False - it provides inference of the predictive power of the model
Forward stepwise will select larger models than backward. - ✔✔ANSWER✔✔>>False -
it will typically select smaller models especially if p is large
From the binomial approximation with a normal distribution using the central limit
theorem, the Pearson residuals have an approximately standard chi-squared
distribution. - ✔✔ANSWER✔✔>>False - Normal distribution
Generally models with covariance have high bias but low variance -
✔✔ANSWER✔✔>>False - they have low bias but high variance.
Goodness of fit tests the null hypothesis that - ✔✔ANSWER✔✔>>the model fits the
data
Hypothesis testing for Poisson regression can be done on small sample sizes -
✔✔ANSWER✔✔>>False - Approximation of normal distribution needs large sample
sizes, so does hypothesis testing.
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 p is larger than n, stepwise is feasible - ✔✔ANSWER✔✔>>TRUE - for forward, but
not backward
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?
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