Mgt 6203 t - Study guides, Class notes & Summaries

Most common problems in fitting linear regression - 1. Non-linearity of the responsepredictor relationships 2. Correlation of error terms 3. Non-Constant variance of error terms 4. Outliers 5. High-leverage points 6. Collinearity How to check for linearity - 1. Check the scatterplot of Y vs. X variable. Is it linear? 2. OR residual plot vs. fitted plot (especially useful in multiple regression). We want to see no patterns in this plot

Summer MGT 6203 MID EXAM PART2 – CODING Week 1 Use the inbuilt dataset ‘longley’ for questions 1 and 2. Q1) Fit a linear regression model with ‘Employed’ as the response variable and all other variables (except ‘Year’) as predictors. What are the significant predictors at 10% significance level? A. GNP B. GNP, Armed.Forces C. GNP, Unemployed, Population D. None of the predictors are significant at 10% significance level Solution: model1 = lm(Employed~.-Year, data = long...

MGT 6203 MIDTERM PART 2– SOLUTION KEY CODING QUESTIONS with ANSWERS Week 1 Use the inbuilt dataset ‘longley’ for questions 1 and 2. Q1) Fit a linear regression model with ‘Employed’ as the response variable and all other variables (except ‘Year’) as predictors. What are the significant predictors at 10% significance level? A. GNP B. GNP, Armed.Forces C. GNP, Unemployed, Population D. None of the predictors are significant at 10% significance level Solution: model1 = lm(E...

Most common problems in fitting linear regression - 1. Non-linearity of the responsepredictor relationships 2. Correlation of error terms 3. Non-Constant variance of error terms 4. Outliers 5. High-leverage points 6. Collinearity How to check for linearity - 1. Check the scatterplot of Y vs. X variable. Is it linear? 2. OR residual plot vs. fitted plot (especially useful in multiple regression). We want to see no patterns in this plot

MGT 6203 MIDTERM – SOLUTION KEY PART 2 CODING QUESTIONS WITH ANSWERS.) Please estimate a linear regression model (using the lm function) with Personal as the dependent variable and Room.Board as the independent variable. What are the model’s Rsquared and adjusted R-squared values? a) 0.00549, 0.048 b) 0.0143, 0.022 c) 0.0398, 0.0385 d) 0.0325, 0.0336 Answer: C (Week 1 Lesson 4) library("ISLR") data("College") summary(lm(College$Personal~College$Room.Board)) ## ## Call: ## lm(formula = Co...