ISYE 6414 REGRESSION MIDTERM 2 EXAM -With 100% verified solutions -2023 True - The relationship that links the predictors is highly non -linear. In Logistic Regression, the relationship between the probability of success an d 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 mo del 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 un it 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 distributi on 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 regress ion 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.
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 mo del 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 un it 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 distributi on 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 regress ion 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.
