Isye 6414 final exam 2023 - Study guides, Class notes & Summaries

Logistic Regression - Commonly used for modeling binary response data. The response variable is a binary variable, and thus, not normally distributed. In logistic regression, we model the probability of a success, not the response variable. In this model, we do not have an error term g-function - We link the probability of success to the predicting variables using the g link function. The g function is the s-shape function that models the probability of success with respect to the predict...

ISYE 6414 Final Exam Graded A+ 2023

ISYE 6414 FINAL EXAM REVIEW 2023 NOVEMBER QUESTIONS 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 Likelihoo...

ISYE 6414 FINAL EXAM / ISYE6414 FINAL EXAM REAL EXAM QUESTION AND ANSWERS

ISYE 6414 FINAL EXAM / ISYE6414 FINAL EXAM REAL EXAM QUESTION AND ANSWERS

ISYE 6414 FINAL EXAM 2022-2023 / ISYE 6414 FINAL EXAM REAL EXAM QUESTION AND ANSWERS 3. If considering only BIC for the model selection criterion, a model with lower BIC is preferred overa model with higher BIC. a. True b. False Sol: True. A lower BIC corresponds to a lower penalized prediction risk, and the statementspecified that is the only thing we are considering. 14. If we do not penalize the training risk, our variable selection method would always prefer morecomplex models...

True - The relationship that links the predictors is highly non-linear. - In Logistic Regression, the relationship between the probability of success and the predicting variables is non-linear. False - In logistic regression, there are no error terms. - In Logistic Regression, the error terms follow a normal distribution. True - the logit function is also known as the log-odds function, which is the ln(P/1-p). - The logit function is the log of the ratio of the probability of success to th...

ISYE 6414 FINAL EXAM / ISYE6414 FINAL EXAM REAL EXAM QUESTION AND ANSWERS

ISYE 6414 Final Exam Review 2023

Least Square Elimination (LSE) cannot be applied to GLM models. - Answer- 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. - Answer- 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 multip...