# Analysis of Variance and Covariance

Analysis of Variance (ANOVA) is used to determine whether there is a difference between three or more categorical sets of values e.g. three species, four types of drug, 7 days of the week. Analysis of Covariance (ANCOVA) on the other hand, while also used to determine whether there is a difference between categorical sets of values (two or more in this case) also takes into account the effect of one or more numerical variables called covariates e.g. three species as categorical variables taking into account the effect of differences in body mass as a numerical covariate.

statistiXL provides a very comprehensive module for the analysis of variance and covariance. Both univariate and multivariate ANOVA and ANCOVA are supported. Factors can be specified as fixed or random and the nesting of factors is also supported. Simplified dialog boxes aid the rapid analysis of full factorial and repeated measures models, while for more advanced analyses a comprehensive dialog box is available that allows custom models to be specified precisely detailing the factors and interactions to be included in the analysis. Post Hoc Tests are provided so that you can drill down into your dataset and see what, if any, the major differences between groups are. Tukey, Student-Newman-Keul and Scheffe test are included so you are not constrained to a single type of analysis.

Results from the ANOVA moduleResults are presented in tabulated form, starting optionally with a table of simple descriptive statistics for each group (e.g. mean, standard error, count etc). The overall test for the model is presented next, followed by individual tests for each effect included in the model. Finally, if post hoc analyses were chosen to be performed, a table of all pairwise group comparisons is presented for each factor and for each test type chosen.

The help file included with statistiXL provides and introduction to Analysis of Variance and a comprehensive range of 17 examples detailing how to use statistiXL to analyse different design models of ANOVA and ANCOVA including Single and Multivariate, Full Factorial and User Defined, Fixed and Random Factors, Nested, Latin Square, Randomised Block, Split Plot and Repeated Measures.