Least squares regression analysis is a common method
Deja Vaughn
Answered question
2022-04-15
Answer & Explanation
rhyclelal80j6
Beginner2022-04-16Added 13 answers
if our ordinary least square regression should work properly. data should fit several assumptions.
- linear parameters should be present in the model.
- data should be selected from the population randomly.
- the expected value of residual should be zero.
- residuals have homogeneous variance.
- residual should follow a normal distribution.
- The independent variable has been measured correctly.
If any of the above assumptions are breached then
then confidence interval, forecast, built by regression model be inefficient or seriously deceiving
- Independence of variable: Independence is very serious in time series regression models
- serial correlation in the errors means that there is a chance for in the model, and extreme serial correlation is often a sydevelopemntmptom of a badly misspecified model.
- violation of normality: computation of confidence intervals and various significance tests for coefficients are all form on the assumptions of normally distributed errors.
- If the error distribution is notably non-normal, confidence intervals may be too broad or too slender.
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