ISYE 6414 Final Exam Review 2022 with complete solution

ISYE 6414 Final Exam Review 2022 with complete solution Least Square Elimination (LSE) cannot be applied to GLM models. Ans***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. Ans***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. Ans***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 Ans***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. Ans***True MLE is used for the GLMs for handling complicated link function modeling in the X-Y relationship. Ans***True In the GLMs the link function cannot be a non linear regression. Ans***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. Ans***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. Ans***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. Ans***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 Ans***True When the data may not be nor