The assumption of homoscedasticity simplifies mathematical and computational treatment.
2.
We reject the null hypothesis of homoscedasticity and conclude that heteroscedasticity is present.
3.
Thus heteroscedasticity is the absence of homoscedasticity.
4.
If " ? " 1 is statistically significant, reject the null hypothesis of homoscedasticity.
5.
Linear regression assumes homoscedasticity, that the error variance is the same for all values of the criterion.
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For example, considerations of homoscedasticity examine how much the variability of data-values changes throughout a dataset.
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However, the basic assumption of homoscedasticity is violated, so a weighted least squares regression must be used.
8.
Another assumption of linear regression is that the variance be the same for each possible expected value ( this is known as homoscedasticity ).
9.
Furthermore, it is also claimed that if the underlying assumption of homoscedasticity is violated, the Type I error properties degenerate much more severely.
10.
When Levene's test shows significance, one should switch to more generalized tests that is free from homoscedasticity assumptions ( sometimes even non-parametric tests ).
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