Changes

Whereas correlation describes the linear association among variables, regression involves the prediction of one quantity from the others. Regression analysis is that broad class of statistics and statistical methods that comprises line, curve, and surface fitting, as well as other kinds of prediction and modeling techniques.

Whereas correlation describes the linear association among variables, regression involves the prediction of one quantity from the others. Regression analysis is that broad class of statistics and statistical methods that comprises line, curve, and surface fitting, as well as other kinds of prediction and modeling techniques.

The simplest type of regression analysis involves fitting a straight line between two variables (Figure 1). In this case, one of the quantities is called the ''independent or predictor variable'' (usually denoted x), while the other is called the ''dependent or predicted variable'' (usually denoted y). This approach is often referred to as ''simple linear regression,'' or y-on-x regression. It leads to the development of an empirical straight-line relationship between the two variables and has the following form:

The simplest type of regression analysis involves fitting a straight line between two variables ([[:file:Correlation-and-regression-analysis fig1.png|Figure 1]]). In this case, one of the quantities is called the ''independent or predictor variable'' (usually denoted x), while the other is called the ''dependent or predicted variable'' (usually denoted y). This approach is often referred to as ''simple linear regression,'' or y-on-x regression. It leads to the development of an empirical straight-line relationship between the two variables and has the following form:

:<math>\widehat{y} = ax + b</math>

:<math>\widehat{y} = ax + b</math>