We can guess that some genetic and nutritive effects are involved, but it might be that simply by transforming the available variables we could obtain some better results. The remainder of the variability is due to some effects (other explanatory variables) that have not been or that could not be measured during this experiment. 1ģ In this particular case, 63 % of the variability of the Weight is explained by the Height, the Age and the Gender. Setting up an ANCOVA After opening XLSTAT, select the XLSTAT / Modeling data / ANCOVA command, or click on the corresponding button of the Modeling Data toolbar (see below). In two other tutorials on linear regression this dataset is also used, with the Height and then the Height and the Age as explanatory variables. The specificity of ANCOVA is that it mixes qualitative and quantitative explanatory variables. The ANCOVA method belongs to a larger family of models called GLM (Generalized Linear Models) as do the linear regression and the ANOVA. Goal of this Analysis of Covariance (ANCOVA) Using the Analysis of Covariance (ANCOVA), we want to find out how the weight of the children varies with their gender (a qualitative variable that takes value f or m), their height and their age, and to verify if a linear model makes sense. They concern 237 children, described by their Gender, Age in months, Height in inches (1 inch = 2.54 cm), and Weight in pounds (1 pound = 0.45 kg). Introduction to Experimental Ecology, New York: Academic Press, Inc. 1 Running an ANCOVA in XLSTAT demoanco.xls Dataset for running an ANCOVA An Excel sheet with both the data and results used in this tutorial can be downloaded by clicking here.

Xlstat multivariate comparison