There are many methods for building both classification and regression models. The following section briefly describes a number of alternative approaches. More details on these approaches are referenced in the further reading section of this chapter.

**Multiple linear regressions**: The method described for simple linear regression can be extended to handle multiple descriptor variables. A similar least squares method is used to generate the equation. The form of the equation is*y*=*a*+*b*_{1}*x*_{1}+*b*_{2}*x*_{2}+ . . . +*b*where_{n}x_{k}*y*is the response,*x*_{1}to*x*are the descriptor variables,_{n}*a*is a constant, and*b*_{1}to*b*are also constants. For example, when attempting to predict a potential customer's credit score (_{n}**CS**) a multiple linear regression equation could be generated. The equation could be based on the number of missed payments to other credit cards (**MP**), the number of years with no missed payments (**NMP**), and the number of good standing loans (**GSL**), as for example in the following equation:**CS**= 15 – 18 ×**MP**+ 12 ×**NMP**+ 10 ×**GSL**

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