(a) In regression analysis, often the researcher will encounter issues of omitted variable bias (OVB) or their included variables are too closely related (multicollinearity). In you own words,
(i) explain what is meant by OVB? (1 marks)
(ii) what is multicollinearity (1 marks)
(iii) How do these problems lead to type1/type 2 errors? (1 marks)
(b) In your own words, describe your understanding of linear regression analysis. What is the causal fallacy? (2 marks)
(c) How is the model fit measured? In your answer describe both the R-squared and SER. (2 marks)
(d) What are dummy variables and how are they interpreted? (1 marks)
(e) As an economist you are interested in diminishing returns. Describe how you would go about modelling this. (2 marks)

Answer rating: 100% (QA)

ai OVB occurs when the model does not include all of the relevant variables This can lead to biased ... View the full answer