"The big coefficient: If we have a simple linear regression model, we have some equation like Y = a1 x1 + a2 x2 + b, right? And x1 and x2 are called the independent variables, and y's the dependant variable. So, for
"The world may be nonlinear, and we've got techniques that help us sort of understand linear functions. So here's the first thing we can do. The first thing we can do is we can just approximate our nonlinear function with a linear
"How do you draw the best possible line through the data? ... it's a lot of data out there. One thing you can do is you can fit that data to linear models. What linear models will do is they'll explain some
"The R-squared of the regression is the fraction of the variation in your dependent variable that is accounted for (or predicted by) your independent variables. (In regression with a single independent variable, it is the same as the square of the correlation
"A special case of categorical modeling is logistic regression. You have to use this model when the dependent variable is ordinal. A page devoted to this problem also comes up shortly. You could also turn simple models like these around and analyze
"Using the linear models, you can draw a line through data and use that line to explain some of the variation in the data. Now typically the world isn't gonna be perfectly linear. There's going to be lots of extra variation left
"Linear models describe a continuous response variable as a function of one or more predictor variables. They can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data. Linear regression is a statistical method used