Although [Vitor Fróis] is explaining linear regression because it relates to machine learning, the post and, indeed, the topic have wide applications in many things that we do with electronics ...

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 ...

In traditional models like linear regression and ANOVA, assumptions such as linearity, independence of errors, homoscedasticity, and normality of residuals are foundational.

In the context of regression, the term “linear” can also refer to a linear model, where the predicted values are linear in the parameters. This occurs when E( Y|X ) is a linear function of a ...

R 2 is a statistical measure of the goodness of fit of a linear regression model (from 0.00 to 1.00), also known as the coefficient of determination. In general, the higher the R 2 , the better ...

One of the most versatile regression diagnostic methods is to plot the residuals r i against the predictors (x i, r i) and the predicted values (ŷ i, r i) ().When noise assumptions are met, these ...