Regression

1)Multicollinearity

* Definition

If independent variables are related to each other, multicollinearity among the variables is said to exist. In general, when two or more independent variables are related to each other, then at least one or more variables are redundant because of their tendency to contribute overlapping information.

* Method used to diagnose / detect

A formal method of detecting the presence of multicollinearity that is widely accepted is use of variance inflation factor (VIF).

The largest VIF is greater than 10 and the tolerance statistic is below than 0.1 (where the tolerance statistic is the reciprocal of the VIF).

* Remedial measures / method use to overcome the problem

1. Making predictions is not a problem as long as we use the predictor variables that follow the same pattern of multicollinearity

2. Use of centered data for predictor variables when using polynomial regression and other regressions with interactions

3. Dropping one or more predictors

4. Add some cases that may break the pattern of multicollinearity

5. Use different data sets to estimate different coefficients

6. Principal component type analysis.

7. Ridge regression – Biased Estimation.

2) Autocorrelation

* Definition

Autocorrelation is the cross-correlation of a signal with itself. Informally, it is the similarity between observations as a function of the time lag between them. It is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal obscured by noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies. It is often used in signal processing for analyzing functions or series of values, such as time domain signals.

* Method used to diagnose/detected

When the error terms in the regression model are positively autocorrelated, the use of ordinary least squares procedures is applied.

* Remedial measures/method to the problem

Addition of...