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ISYE 6414 REGRESSION MIDTERM 2 EXAM-With 100% verified solutions-2023
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ISYE 6414 REGRESSION MIDTERM 2 EXAM-With 100% verified solutions-2023
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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.
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