Isye 6414 fin Study guides, Study notes & Summaries

Final Exam – Part 1 Solutions 1. We should always use mean squared error to determine the best value of lambda in lasso regression. a. True b. False Sol: False. The criterion used is a choice we make. 2. Standard linear regression is an example of a generalized linear model where the response is normally distributed and the link is the identity function. a. True b. False Sol: True. See Unit 4.4.1. 3. Goodness-of-fit assessment for logistic regression involves checking for the indepe...

True - The relationship that links the predictors is highly non-linear. - Answer- 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. - Answer- 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). - Answer- The logit function is the log of the ratio of the prob...

ISYE 6414 FINAL EXAM (REAL EXAM) QUESTIONS AND ANSWERS 2022-2024/ GRADED A | EXAM 1

ISYE 6414 Final Exam Review 2022 with complete solution

ISYE 6414 Final Exam Review-with 100% verified solutions-2022-2024

ISYE 6414 Final Exam Review Questions and Answers 100% Pass 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...

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 regres...

ISYE 6414 Final Exam Questions and Answers Already Graded A 1. If there are variables that need to be used to control the bias selection in the model, they should forced to be in the model and not being part of the variable selection process. True 2. Penalization in linear regression models means penalizing for complex models, that is, models with a large number of predictors. True 3. Elastic net regression uses both penalties of the ridge and lasso regression and hence combines the benefits ...