Isye 6414 final exa Study guides, Class notes & Summaries

ISYE 6414 FINAL EXAM REVIEW QUESTIONS AND 100% CORRECT ANSWERS 2024

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

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ISYE 6414 Final Exam; Questions and Answers 100% Verified Logistic Regression Answer-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 Answer-We link the probability of success to the predicting variables using the g link function. The g function is the s-shape f...

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 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...