ISYE 6414 Bundle 2024/2025 with complete solution

If λ=1 - Answer-we do not transform non-deterministic - Answer-Regression analysis is one of the simplest ways we have in statistics to investigate the relationship between two or more variables in a ___ way random - Answer-The response variable is a ___ variable, because it varies with changes ...

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-fu...

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...

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-...

1. All regularized regression approaches can be used for variable selection. - False 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 regr...

Linearity/Mean zero assumption - Means that the expected value (deviances) of errors is zero. This leads to difficulties in estimating B0 and means that our model does not include a necessary systematic component Constant variance assumption - Means that it cannot be true that the model is more a...

Regression Estimator Properties - Unbiasedness: This is the property that the expectation of the estimator is exactly the true parameter. What this means is that Beta_1_hat is an unbiased estimator for Beta_1 Model Parameter Interpretation - a positive value for Beta_1, then that's consistent wi...

For assessing the normality assumption of the ANOVA model, we can only use the quantile-quantile normal plot of the residuals. - False In simple linear regression models, we loose three degrees of freedom because of the estimation of the three model parameters, B0, B1, and Sigma^2? - False In ev...

Using MLE, can we derive estimated coefficients/parameters in exact form? - No, they are approximate estimated parameters T/F: The sampling distribution of the predicted response variable used in statistical inference is normal in multiple linear regression under the normality assumption. - F Cl...

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 - False - poisson regression models rate or count...

σ^2 (sample distribution of the variance estimator) - is chi-squared distribution with n - 2 degrees of freedom (We lose two degrees of freedom because we replaced the two parameters ß0 and ß1 with their estimators to obtain the residuals.) constant variance assumption - which means that the ...

The number of degrees of freedom of the χ 2 (chi-square) distribution for the pooled variance estimator is N − k + 1 where k is the number of samples. - False If the confidence interval for a regression coefficient contains the value zero, we interpret that the regression coefficient is defini...