ISYE 6414 Study guides, Class notes & Summaries

ISYE 6414 REGRESSION MIDTERM 2 REAL EXAM QUESTIONS WITH VERIFIED ANSWERS LATEST UPDATE 2023/2024 In logistic regression, the relationship between the probability of success and the predicting variables is nonlinear. --CORRECT ANSWER--> TRUE: The equation that links the predictors to the probability is:

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 Midterm Prep | 2024 Questions & Answers | 100% Correct | Verified We can assess the constant variance assumption in linear regression by plotting the residuals vs. fitted values. - True If one confidence interval in the pairwise comparison in ANOVA includes zero, we conclude that the two corresponding means are plausibly equal. - True The assumption of normality is not required in linear regression to make inference on the regression coefficients. - False (Explanation: i...

ISYE 6414 Midterm Summer Study Guide | 2024 Questions & Answers | 100% Correct | Verified Assuming that the residuals are normally distributed, the estimated variance of the error terms has the following sampling distribution under SLR: - Chi-square with n-2 degrees of freedom fitted values def - the regression line with parameters replaced with the estimated regression coefficients the estimators of the linear regression model are derived by - minimizing the sum of squared diffe...

Assuming that the data are normally distributed, under the simple linear model, the estimated variance has the following sampling distribution: - Chi-squared with n-2 degrees of freedom. The fitted values are defined as? - The regression line with parameters replaced with the estimated regression coefficients. The estimators fo the linear regression model are derived by? - Minimizing the sum of squared differences between the observed and expected values of the response variable. The estim...

We can assess the constant variance assumption in linear regression by plotting the residuals vs. fitted values. - True If one confidence interval in the pairwise comparison in ANOVA includes zero, we conclude that the two corresponding means are plausibly equal. - True The assumption of normality is not required in linear regression to make inference on the regression coefficients. - False (Explanation: is required) We cannot estimate a multiple linear regression model if the predicting v...

Assuming that the residuals are normally distributed, the estimated variance of the error terms has the following sampling distribution under SLR: - Chi-square with n-2 degrees of freedom fitted values def - the regression line with parameters replaced with the estimated regression coefficients the estimators of the linear regression model are derived by - minimizing the sum of squared differences between observed and expected values of the response variable the estimators for the regressi...

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 multiple ...

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. - Answer-True 2. Penalization in linear regression models means penalizing for complex models, that is, models with a large number of predictors. - Answer-True 3. Elastic net regression uses both penalties of the ridge and lasso regression and hence combines the benefits of both. - Answer-True 4. Variable sele...

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 function that models the probability of success with respect to...

ISYE 6414 Midterm Prep Exam Questions with Verified Solutions

ISYE 6414 Midterm, Summer 2024- Questions with Answers