This edition: Regression
By keeping track of variables like bowling scores or chips in a poker game, one can predict how well someone will do in the next event. Statisticians are much more exact on how they predict what will happen to a given set of variables. We learned this in the previous lesson on scatterplots. There, the explanatory variable (the variable believed to cause an affect) was plotted on the x-axis, and the response variable (the other variable, which represents the outcome) was plotted on the y-axis. Graphing the data in that way allows one to more easily see if any relationship or trend exists between the variables.
One common association that may appear on a scatterplot occurs when the response variable y changes at the same rate as the explanatory variable x. When this happens, the scatterplot of the association reveals data points distributed around what appears to be straight line, something known as a linear relationship. In this lesson, you will take a closer look at linear relationships, and discover how statisticians use them to make predictions through a powerful statistical method called regression.