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 ...

What Is Linear Regression and How Does it Work? At the most basic level, linear regression relies on one variable—the independent variable—to predict the value of another variable: the ...

Linear regression is much more flexible than its name might suggest, including polynomials, ANOVA and other commonly used statistical methods. References Box, G. J. Am. Stat. Assoc. 71 , 791–799 ...

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 ...

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 ...

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.