ISYE 6414 Final Exam Review Complete Questions And Answers

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 linear regression. - Answer-False - In SLR and MLR, the SLE and MLE are the same with normal idd data. The backward elimination requires a pre-set probability of type II error - Answer-False - Type I error The first degree of freedom in the F distribution for any of the three procedures in stepwise is always equal to one. - Answer-True MLE is used for the GLMs for handling complicated link function modeling in the X-Y relationship. - Answer-True In the GLMs the link function cannot be a non linear regression. - Answer-False - It can be linear, non linear, or parametric When the p-value of the slope estimate in the SLR is small the r-squared becomes smaller too. - AnswerFalse - When P value is small, the model fits become more significant and R squared become larger. In GLMs the main reason one does not use LSE to estimate model parameters is the potential constrained in the parameters. - Answer-False - The potential constraint in the parameters of GLMs is handled by the link function. The R-squared and adjusted R-squared are not appropriate model comparisons for non linear regression but are for linear regression models. - Answer-TRUE - The underlying assumption of R-squared calculations is that you are fitting a linear model.The decision in using ANOVA table for testing whether a model is significant depends on the normal distribution of the response variable - Answer-True When the data may not be normally distributed, AIC is more appropriate for variable selection than adjusted R-squared - Answer-True The slope of a linear regression equation is an example of a correlation coefficient. - Answer-False - the correlation coefficient is the r value. Will have the same + or - sign as the slope. In multiple linear regression, as the value of R-squared increases, the relationship between predictors becomes stronger - Answer-False - r squared measures how much variability is explained by the model, NOT how strong the predictors are. When dealing with a multiple linear regression model, an adjusted R-squared can be greater than the corresponding unadjusted R-Squared value. - Answer-False - the adjusted rsquared value take the number and types of predictors into account. It is lower than the r squared value. In a multiple regression problem, a quantitative input variable x is replaced by x − mean(x). The R-squared for the fitted model will be the same - Answer-True The estimated coefficients of a regression line is positive, when the coefficient of determination is positive. - Answer-False - r squared is always positive. If the outcome variable is quantitative and all explanatory variables take values 0 or 1, a logistic regression model is most appropriate. - Answer-False - More research is necessary to determine the correct model. After fitting a logistic regression model, a plot of residuals versus fitted values is useful for checking if model assumptions are violated. - Answer-False - for logistic regression use deviance residuals.In a greenhouse experiment with several predictors, the response variable is the number of seeds that germinate out of 60 that are planted with different treatment combinations. A Poisson regression model is most appropriate for modeling this data - Answer-False - poisson regression models rate or count data. For Poisson regression, we can reduce type I errors of identifying statistical significance in the regression coefficients by increasing the sample size. - Answer-True Both LASSO and ridge regression always provide greater residual sum of squares than that of simple multiple linear regression. - Answer-True