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Heteroscedasticity Definition

In regression analysis the condition of nonconstant variance.

Heteroscedasticity is the condition of nonconstant variance in regression analysis. In marketing research, this condition happens when the variance of the error term, also called the residual variance, is not constant across all levels of the independent variable or variables. On a graph, the condition indicates the spread of data points around the regression line changes as the values of the independent variable change. This violates an assumption of linear regression, which assumes homoscedasticity. Dealing with the possibility of heteroscedasticity maintains research integrity because the presence of the condition could lead to wrong and unreliable conclusions.Identifying the variation will ensure that regression models are more accurate.

Who monitors heteroscedasticity?

Researchers, analysts and marketing professionals search to locate and understand heteroscedasticity so they can identify whether homoscedasticity violates regression models.

Modern regression techniques (like robust standard errors, weighted least squares, or generalized linear models) are commonly used to mitigate heteroscedasticity.

Why should I care about heteroscedasticity?

Encountering the condition of heteroscedasticity in marketing research can lead to inaccurate and unreliable results in regression analyses. When heteroscedasticity is present, the standard errors of the coefficients can be biased, leading to incorrect t-tests and p-values. This condition can impact the interpretation of relationships between variables and the validity of research conclusions.