**Homoskedastic, **

### Definition of Homoskedastic:

Homoskedasticity is one assumption of linear regression modeling. If the variance of the errors around the regression line varies much, the regression model may be poorly defined. The opposite of homoskedasticity is heteroskedasticity just as the opposite of "homogenous" is "heterogeneous." Heteroskedasticity (also spelled “heteroscedasticity”) refers to a condition in which the variance of the error term in a regression equation is not constant.

A type of error structure often used in statistics that indicates that the variance of errors over the entire sample are similar. There will be no pattern or tendency shown if the error variance around the line of best fit varies.

Homoskedastic (also spelled "homoscedastic") refers to a condition in which the variance of the residual, or error term, in a regression model is constant. That is, the error term does not vary much as the value of the predictor variable changes. However, the lack of homoskedasticity may suggest that the regression model may need to include additional predictor variables to explain the performance of the dependent variable.

### How to use Homoskedastic in a sentence?

- Oppositely, heteroskedasticity occurs when the variance of the error term is not constant.
- Adding additional predictor variables can help explain the performance of the dependent variable.
- If the variance of the error term is homoskedastic, the model was well-defined. If there is too much variance, the model may not be defined well. .
- Homoskedasticity occurs when the variance of the error term in a regression model is constant. .

Meaning of Homoskedastic & Homoskedastic Definition