Binary logit models Samenvattingen, Aantekeningen en Examens

Summary of Quantitative Innovation Analytics with the topics: 1) Introduction to the course and quantitative models, 2) Theory and research designs, 3) Linear regression in R, 4) Binary logit models, 5) Multilevel regression, 6) Count Models, and 7) Time to event analysis.

ISYE 6414 – Final Exam Questions and Answers 100% Correct 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...

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...

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...

In logistic regression, we model the__________________, not the response variable, given the predicting variables. - Answer-probability of a success g link function - Answer-link the probability of success to the predicting variables 3 assumptions of the logistic regression model - Answer-Linearity, Independence, Logit link function Linearity assumption for a Logistic Model - Answer-Similar to the regression model we have learned in the previous lectures, the relationship we assume now, bet...

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...

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 the...

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 ...

What is cooks distance used for? - correct answer It measures how much all of the values in the regression model change with the ith observation is removed. Basically its a test for outliers Rule of thumb: D denotes cooks distance, if D is > 4/n OR D > 1 or any large D then it may be an outlier and should be removed. If the normality assumption does not hold, we can pursue a transformation in the response variable. T/F - correct answer ...

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...