We use regression analysis to explore the relationships between one or more input variables, or factors, and a response. A manufacturer might use it to look at how baking time and temperature relate to the hardness of a piece of plastic. Social scientists might use it to see how educational levels and birthplace relate to annual income. In theory, the number of factors you could include in a regression model is limited only by your imagination.
But before we throw data about every potential predictor under the sun into a regression model, we need to remember a thing called multicollinearity. In regression, as in so many things, there comes a point where more is not better. Sometimes adding more factors to a regression model not only fails to make relationships clearer, it actually makes them harder to understand.