We explain linear regression models with multiple predictors, including an overview of partial regression coefficients. The related concept of partial correlation is discussed in a separate section. We also contrast the overall model test using the F-ratio statistic and the t tests of partial effects of individual predictors. The adjusted coefficient of determination is presented as a more accurate way of conveying the explanatory power of a regression model. Finally, we characterise the family of general linear models, focusing specifically on analysis of covariance (ANCOVA). We provide examples of ANCOVA models and demonstrate their usefulness when applied to the analysis of biological experiments. The methods described in this chapter are accompanied by a carefully-explained guide to the R code needed for their use, including the effects and ppcor packages.
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