Questions:

- Why are joint F-tests of significance more reliable than t-tests of significance for regression coefficients?
- How can multicollinearity distort the estimation of regression coefficients?

- What are some common-sense remedies for multicollinearity?

- Why is the R-squared a particularly awful, detestable way to evaluate a regression equation?

- Why makes the Durbin-Watson statistic so “ elementary”?

- What is autocorrelation so big a deal when interpreting a regression coefficient?

- What are the differences between the Akaike and the Schwartz information criteria?

- For assessing the Granger test, why are F-tests used and not t-tests?

- Under what conditions, would autocorrelation not be a problem?

- When adding new variables as arguments in a regression model, what does it tell us if we find that the coefficients of the previously included variables change drastically? What does it tell us if they do not change at all?

- What is the danger of “overfitting” a regression equation, by putting in a lot of regressors to get a good in-sample fit?

- What is the difference between an unbiased and a consistent estimator? Give a simple example.

**You can answer each question in about two or three sentences. Again, put some thought into your answer but this is not a major essay. Your goal is to show what is going on with the key concepts we have discussed in class so far. As presented in class, the essays may be done with a referral to formulate, but all of the written material in the essay is original, “synthetic” independent work, with no copying or published or unpublished material from the web or any other source.**

**There is to be no collaboration or collusions with other students. Answer in your own words, you are free to consult notes and the text, but do not cut and paste from the web. **

**As you answer each question, please paste in the question number and question before your answer.**

Subject | Science |

Due By (Pacific Time) | 10/16/2015 08:00 pm |

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