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Interpretation and Steps to Test Heteroskedasticity

Interpretation and Steps to Test Heteroskedasticity

Heteroskedasticity is useful to examine whether there is a difference in the residual variance of the observation period to another period of observation. A Good regression model is not the case heteroscedasticity problem.

Statistical methods to test Heteroskedasticity

Many statistical methods are there to determine whether a model is free from the problem of heteroscedasticity or not, like

1. White Test,


2. Test Park,


3. Test Glejser.

SPSS Test will introduce one of heteroscedasticity test that can be applied in SPSS, namely

Test Glejser. Glejser test conducted by regressing absolud residual value of the independent

variable with regression equation is: Ut = A + B Xt + vi

Interpretation of Heteroskedasticity Test with Test Glejser (SPSS)

1. If the value Sig. > 0.05, then there is no problem of heteroscedasticity


2. If the value Sig. <0.05, then there is a problem of heteroscedasticity

Based on Output from the above table, Coefficients obtained value of Sig. Competence variable of 0.834, and the Sig. Motivation variable of 0.348, meaning that the value of the variable sig Competence and Motivation > 0.05, it can be concluded that there is no heteroscedasticity problem.

http://www.spsstests.com/2015/03/test-heteroskedasticity-glejser-using.html

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