homoskedastsus
Homoskedasticity is a key assumption in statistical modeling, particularly in regression analysis. It refers to a situation where the variance of the errors (or residuals) in a statistical model is constant across all levels of the independent variables. In simpler terms, the spread of the data points around the regression line is the same everywhere. This is in contrast to heteroskedasticity, where the variance of the errors changes.
The assumption of homoskedasticity is important because many statistical methods, such as ordinary least squares (OLS)
Detecting homoskedasticity typically involves visual inspection of residual plots, where residuals are plotted against the predicted
If heteroskedasticity is detected, several remedies can be employed. These include using robust standard errors, transforming