ISYE6414 Midterm 2 Questions And Answers With Verified Solutions

The _______________ for multiple linear regression do not have constant variance. - Answer-sample residuals The ______________ for multiple linear regression have constant variance. - Answer-error terms QQPlot and histogram are used to assess what in MLR? - Answer-normality Residuals vs predictor are used to predict what in MLR - Answer-linearity Residuals vs fitted values are used to assess what in MLR - Answer-constant variance and independence Points that are far from the mean of the x's are called - Answer-leverage points Points that are far from the mean of the x's and y's are called - Answer-Influential points It is good practice to perform regression analysis with and without what? - Answer-outliers What is used to quantify outliers? - Answer-Cook's distance How much all the values in the model change when the ith value is removed is known as what? - Answer-Cook's distance Cooks distance that should be investigated. - Answer-D_i > 4/n or D_i > 1 or Large D The proportion of variability in Y than can be explained by the predictor variables. - Answer-R-Squared Model variables used to account for selection bias. - Answer-Controlling factorsContinuous variables are converted to ________ when there is a distinct gap in a variable distribution. - Answer-Indicator variable The number of degrees of freedom for a T test for the statistical significance of a MLR coefficient? - Answer-n-p-1 When the regression model has a high F value/low p-value. - Answer-At least one variable has explainitory power on the response variable. When testing subsets of coefficients using anova command. - Answer-order matters When can "year" be used as a qualitative variable? - Answer-If the variable is not very granular Used to evaluate the relationship between any two qualitative variables. - Answer-Pearson Chi-squared test A command needed prior to running a pearson chi-squared test of qualitative variables. - Answer-table What should you do when you have a high number of predicting variables due to a large number of categorical variables resulting in numerous dummy variables. - Answer-Reduce the dummy variables into groups Which category does R choose as the baseline label when creating dummy variables with r() - Answer-The first If you use a model without an intercept, how will interpreting coefficients be different? - Answer-No baseline for comparison The statistical significance of a predicting variable in a marginal and conditional models are _____________ . - Answer-independentNo analysis of prediction is complete without evaluating the performance of the model using this technique. - Answer-cross validation Higher number of folds in k-fold cross validation means what? - Answer-less bias For logistic regression, the statistical inference based on the normal distribution applies only under what? - Answer-large samples In goodness of fit tests, what is the null hypothesis? - Answer-Model is a good fit In MLR, the F test is used to evaluate the overall regression. - Answer-True In MLR, the coefficient of variation is interpreted as the percentage of variability in the response variable explained by the model. - Answer-True Residual analysis is used to measure predictive value of a model. - Answer-False In the presence of multicollinearity, the coefficient of variation decreases. - Answer-False In the presence of multicollinearity, the regression coefficients will tend to be identified as statistically significant even if they are not. - Answer-False In the presence of multicollinearity, the prediction will not be impacted. - Answer-False If the linearity assumption with respect to one or more predictors does not hold, then we use transformations of the corresponding predictors to improve on this assumption. - Answer-True If the normality assumption does not hold, we transform the predictive variables, commonly using the Box-Cox transformation. - Answer-False If the constant variance assumption does not hold, we transform the response variable. - Answer-True