ISYE 6414 Final Exam Review Updated 2024/2025 Verified 100%
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Course
ISYE 6414
Institution
ISYE 6414
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 - False - poisson regression models rate or coun...
ISYE 6414 Final Exam Review
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 - False - poisson regression models rate or count data.
The R-squared and adjusted R-squared are not appropriate model comparisons for non linear regression
but are for linear regression models. - TRUE - The underlying assumption of R-squared
calculations is that you are fitting a linear model.
Least Square Elimination (LSE) cannot be applied to GLM models. - 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. - 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. - 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 - 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. - True
MLE is used for the GLMs for handling complicated link function modeling in the X-Y relationship. -
True
, In the GLMs the link function cannot be a non linear regression. - 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. -
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. - False - The potential constraint in the parameters of GLMs is
handled by the link function.
The decision in using ANOVA table for testing whether a model is significant depends on the normal
distribution of the response variable - True
When the data may not be normally distributed, AIC is more appropriate for variable selection than
adjusted R-squared - True
The slope of a linear regression equation is an example of a correlation coefficient. - 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 - 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. - 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 - True
The estimated coefficients of a regression line is positive, when the coefficient of
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