1. State and explain the major assumptions of OLS regression analysis. What are the requirements for...

 

Transcribed Image Text:

1. State and explain the major assumptions of OLS regression analysis. What are the requirements for an estimator to be BLUE? If the normality assumption is violated, can you still consider your OLS estimator BLUE? What is the implication of violating normality assumption is regression analysis? 2. State and explain the major components of simple and multiple regression models. 3. Be able to interpret dummy variables in the regression results. 4. Name and explain some of the possible regression models if your dependent variables are: Binomial, Categorical Count, Inflated zeros Left trimmed or right trimmed. 5. Discuss the major differences/similarities between simple and multiple regression analysis. 1. State and explain the major assumptions of OLS regression analysis. What are the requirements for an estimator to be BLUE? If the normality assumption is violated, can you still consider your OLS estimator BLUE? What is the implication of violating normality assumption is regression analysis? 2. State and explain the major components of simple and multiple regression models. 3. Be able to interpret dummy variables in the regression results. 4. Name and explain some of the possible regression models if your dependent variables are: Binomial, Categorical Count, Inflated zeros Left trimmed or right trimmed. 5. Discuss the major differences/similarities between simple and multiple regression analysis.

Answer rating: 100% (QA)

Assumptions of OLS Regression Analysis a Linearity The relationship between the dependent variable and the independent variables is linear in the population model b Independence The observations are i... View the full answer