lhomoscedasticité
Homoscedasticity refers to a statistical assumption where the variance of the errors in a regression model is constant across all levels of the independent variables. In simpler terms, it means that the spread of the data points around the regression line is roughly the same for all values of the predictor variables. This is often visually represented by a scatter plot where the points are evenly distributed around the regression line, without any discernible funnel or fan shape.
The opposite of homoscedasticity is heteroscedasticity, where the variance of the errors is not constant. This
Common methods for detecting heteroscedasticity include visual inspection of residual plots (plotting residuals against predicted values