Experimental Design and Data Analysis for Biologists

The model fitting and estimation approach is laid out using two simple linear models, one for a continuous biological predictor variable and one for a categorical predictor. These two models are the familiar simple linear regression and the single-factor ANOVA. We show how these two models are variations on a theme and describe how to fit them to data. The model fitting is treated in detail, laying the foundation for more complex models in the following chapters. We emphasize what the model parameters mean, how to estimate them, calculate standard errors and confidence intervals, and test hypotheses about them. For categorical predictors, we introduce and recommend planned comparisons (contrasts) to examine patterns across categories. Checking assumptions and identifying unusual and influential data is detailed, as is the use of power analysis to determine necessary sample sizes.