10: ANCOVA Part II

Analysis of Variance and Design of Experiments · LibreTexts

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    Objectives

    Upon completion of this chapter, you should be able to:

    • Use ANCOVA to analyze experiments that require polynomial modeling for quantitative (numerical) predictors.
    • Test hypotheses for treatment effects on polynomial coefficients.

    In this chapter, we will extend our work with ANCOVA to model quantitative predictors with higher-order polynomials by utilizing orthogonal polynomial coding. Fitting a polynomial to express the impact of the quantitative predictor on the response is also called trend analysis and helps to evaluate the separate contributions of linear and nonlinear components of the polynomial. The examples discussed will illustrate how software can be used to fit higher-order polynomials within an ANCOVA model.

    This page titled 10: ANCOVA Part II was last modified on Mon, 02 Jan 2023 23:39:58 GMT and is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by Penn State's Department of Statistics via source content that was edited to the style and standards of the LibreTexts platform.