When dealing with quantitative factors, typically we assume some kind of continuity across varying levels. A typical research hypothesis assumes that the data can be approximated by a polynomial equation of a certain degree with a certain accuracy. The mathematical approximation theory often provides the proper framework, and the statistical approach only follows a typically involved analytical theory.

The commonly used approximation is by orthogonal polynomials. In this article we provide basic information on orthogonal polynomials, as used in statistics. We also assemble a body of related mathematical facts which help in generalizing the approach and constructing one's own orthogonal contrasts adapted to specific problems.

See the attached documents for specific information.

Attachment | Size |
---|---|

An Introduction to orthogonal polynomials | 177.53 KB |

The grain study in PDF format | 80.11 KB |

The above in TeXmacs format | 37.26 KB |

- Log in to post comments