- Explain any three assumptions of the classical linear regression model. - Expl
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Question
- Explain any three assumptions of the classical linear regression model.
- Explain the role of the random distrubance term in regression analysis.
- What is the simple probability sampling?
- Explain the importance of accurate sampling in statistical analysis.
- Explain the importance of diagnostic test.
- Outline and describe any two types of diagnostic tests and explain the remedies to the problems that may be detected.
- Name two diagnostic tests that be applied to both time series and cross sectional models.
- Do all random variables have distribution functions? Please explain your answer.
- Explain the usefulness of information criterions in econometric modelling
Explanation / Answer
Meaningful regression analysis requires the following:
(a) The values of the independent variable should be themselves independent (no relation should exist between them).
(b) There should be a good rational or experimental basis for identifying the independent variables and the resultant dependent variable. This is required because regression models can be built both ways- x vs y and y vs x, and they will be different.
(c) Good (sufficient) sample size, with the pairs of units sampled randomly
(d) The random error terms e are independent and, for any value of x, have a normal distribution with = 0 and variance ^2
If these requirements are not satisfied, the model will not be robust.
The analysis of residuals plays an important role in validating the regression model. If the error term in the regression model satisfies the assumptions for regression, then the model is considered valid.
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