In regression analysis we fit a predictive model to our data and use that model to predict values of the dependent variable from one or more independent variables.

Simple regression seeks to predict an outcome variable from a single predictor variable whereas multiple regression seeks to predict an outcome from several predictors. We can predict any data using the following general equation:

(Outcome)i = (Model)i + (Error)i

The model that we fit here is a linear model. Linear model just means a model based on a straight line. One can imagine it as trying to summarize a data set with a straight line.