What happens if variance is not constant?

Heteroskedasticity is when the variance of the error term, or the residual variance, is not constant across observations. Graphically, it means the spread of points around the regression line is variable.

How do you test for homoscedasticity?

A scatterplot of residuals versus predicted values is good way to check for homoscedasticity. There should be no clear pattern in the distribution; if there is a cone-shaped pattern (as shown below), the data is heteroscedastic.

How do you test for homogeneity of variance in SPSS?

The steps for assessing the assumption of homogeneity of variance for ANOVA in SPSS

  1. Click Analyze.
  2. Drag the cursor over the Compare Means drop-down menu.
  3. Click on One-way ANOVA.
  4. Click on the continuous outcome variable to highlight it.
  5. Click on the arrow to move the outcome variable into the Dependent List: box.

How do you know if variance is constant?

If the spread of the residuals is roughly equal at each level of the fitted values, we say that the constant variance assumption is met. Otherwise, if the spread of the residuals systematically increases or decreases, this assumption is likely violated.

What is the difference between homoscedasticity and heteroscedasticity?

When the residuals are observed to have unequal variance, it indicates the presence of heteroskedasticity. However, when the residuals have constant variance, it is known as homoskedasticity. Homoskedasticity refers to situations where the residuals are equal across all the independent variables.

How do you analyze heteroscedasticity?

One of the most common ways of checking for heteroskedasticity is by plotting a graph of the residuals. Visually, if there appears to be a fan or cone shape in the residual plot, it indicates the presence of heteroskedasticity.

What is Homoscedasticity in SPSS?

Homoscedasticity refers to whether these residuals are equally distributed, or whether they tend to bunch together at some values, and at other values, spread far apart. In the context of t-tests and ANOVAs, you may hear this same concept referred to as equality of variances or homogeneity of variances.