Perhaps unnoticed, all the data we have been using is for a single variable. The type of data described in the examples above and for any model of cause and effect is bivariate data — "bi" for two variables.

In reality, statisticians use multivariate data, meaning many variables As we begin this section we note that the type of data we will be working with has changed.

Linear — a model that takes data and regresses it into a straight line equation. Bivariate — two variables are present in the model where one is the “cause” or independent variable and the other is the “effect” of dependent variable. Multivariate — a system or model where more than one independent variable is being used to predict an outcome. There can only ever be one dependent variable, but there is no limit to the number…. Sum of Squared Errors (SSE) — the calculated value from adding up all the squared residual terms. The hope is that this value is very small when creating a model.