ISYE 6414 Final Exam Review || with A+ Guaranteed Solutions.
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ISYE 6414
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ISYE 6414
Least Square Elimination (LSE) cannot be applied to GLM models. correct answers False - it is applicable but does not use data distribution information fully.
In multiple linear regression with idd and equal variance, the least squares estimation of regression coefficients are always unbiased. c...
ISYE 6414 Final Exam Review || with A+ Guaranteed
Solutions.
Least Square Elimination (LSE) cannot be applied to GLM models. correct answers False - it is
applicable but does not use data distribution information fully.
In multiple linear regression with idd and equal variance, the least squares estimation of
regression coefficients are always unbiased. correct answers True - the least squares estimates
are BLUE (Best Linear Unbiased Estimates) in multiple linear regression.
Maximum Likelihood Estimation is not applicable for simple linear regression and multiple
linear regression. correct answers False - In SLR and MLR, the SLE and MLE are the same with
normal idd data.
The backward elimination requires a pre-set probability of type II error correct answers False -
Type I error
The first degree of freedom in the F distribution for any of the three procedures in stepwise is
always equal to one. correct answers True
MLE is used for the GLMs for handling complicated link function modeling in the X-Y
relationship. correct answers True
In the GLMs the link function cannot be a non linear regression. correct answers False - It can be
linear, non linear, or parametric
When the p-value of the slope estimate in the SLR is small the r-squared becomes smaller too.
correct answers False - When P value is small, the model fits become more significant and R
squared become larger.
In GLMs the main reason one does not use LSE to estimate model parameters is the potential
constrained in the parameters. correct answers False - The potential constraint in the parameters
of GLMs is handled by the link function.
The R-squared and adjusted R-squared are not appropriate model comparisons for non linear
regression but are for linear regression models. correct answers TRUE - The underlying
assumption of R-squared calculations is that you are fitting a linear model.
The decision in using ANOVA table for testing whether a model is significant depends on the
normal distribution of the response variable correct answers True
When the data may not be normally distributed, AIC is more appropriate for variable selection
than adjusted R-squared correct answers True
, The slope of a linear regression equation is an example of a correlation coefficient. correct
answers False - the correlation coefficient is the r value. Will have the same + or - sign as the
slope.
In multiple linear regression, as the value of R-squared increases, the relationship
between predictors becomes stronger correct answers False - r squared measures how much
variability is explained by the model, NOT how strong the predictors are.
When dealing with a multiple linear regression model, an adjusted R-squared can
be greater than the corresponding unadjusted R-Squared value. correct answers False - the
adjusted rsquared value take the number and types of predictors into account. It is lower than the
r squared value.
In a multiple regression problem, a quantitative input variable x is replaced by x −
mean(x). The R-squared for the fitted model will be the same correct answers True
The estimated coefficients of a regression line is positive, when the coefficient of
determination is positive. correct answers False - r squared is always positive.
If the outcome variable is quantitative and all explanatory variables take values 0 or
1, a logistic regression model is most appropriate. correct answers False - More research is
necessary to determine the correct model.
After fitting a logistic regression model, a plot of residuals versus fitted values is
useful for checking if model assumptions are violated. correct answers False - for logistic
regression use deviance residuals.
In a greenhouse experiment with several predictors, the response variable is the
number of seeds that germinate out of 60 that are planted with different treatment
combinations. A Poisson regression model is most appropriate for modeling this
data correct answers False - poisson regression models rate or count data.
For Poisson regression, we can reduce type I errors of identifying statistical
significance in the regression coefficients by increasing the sample size. correct answers True
Both LASSO and ridge regression always provide greater residual sum of squares
than that of simple multiple linear regression. correct answers 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. correct answers 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. correct answers False - must see
a comparison of data points. Is 1 too large?
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