ISYE 6414 Final Exam Questions With 100% Correct Answers.

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 probability of failure and is also known as the log-odds function. False - As there is no error term in logistic regression, there is no additional parameter for the variance of the error terms. - The number of parameters that need to be estimated in a logistic regression model with 6 predicting variables and an intercept is the same as the number of parameters that need to be estimated in a standard linear regression model with an intercept and same predicting variables. False - log-likelihood is a non-linear function, and a numerical algorithm is needed in order to maximize it. - The log-likelihood function is a linear function with a closed form solution. False - We interpret logistic regression coefficients with respect to the odds of success. - In Logistic Regression, the estimated value for a regression coefficient B represents the estimated expected change in the response variable associated with a one unit increase in the predicting variable, holding all else fixed. False - The coefficient estimator follows an approximate normal distribution. -Under logistic regression, the sampling distribution used for a coefficient estimator is a chi-square distribution when the sample size is large. False - when testing a subset of coefficients, deviance follows a chi-square distribution with q degrees of freedom, where q is the number of regression coefficients discarded from the full model to get the reduced model. - When testing a subset of coefficients, deviance follows a chi-square distribution with q degrees of freedom, where q is the number of regression coefficients in the reduced model.True - logistic regression is the generalization of the standard regression model that is used when the response variable y is binary or binomial. - Logistic regression deals with the case where the dependent variable is binary and the conditional distribution is binomial. False - The residuals can only be defined for logistic regression with replications. - It is good practice to perform a goodness-of-fit test on logistic regression models without replications. False - for logistic regression, if the p-value of the deviance test for GOD is large, then the model is a good fit. - In Logistic regression, if the p-value of the deviance test for GOF is smaller than the significance level alpha, then is is plausible that the model is a good fit. False - GOF is no guarantee for good prediction and vice-versa. - If a logistic regression model provides accurate classification, then we can conclude that it is a good fir for the data