An Analysis of Perennial Crop Data

In many fields of study, multiple observations are made on each individual. Treatment of such multivariate data differs. A trait-bytrait analysis may be made. Methods which consider several characters simultaneously include variance, covariance, components of variance and regression analysis. One of these may adequately answer the questions raised or test the hypotheses stated by the research worker when designing the experiment or survey. However, cases arise where none of these procedures is wholly adequate or appropriate. A multivariate analysis may be both appropriate and adequate in such cases. The term multivariate analysis will be applied to analyses of data where several variables are considered jointly with none relegated to the position of an independent variable. If such multiple observations are analyzed on the basis of separate variables, the combination of the results of univariate tests and the assignment of a measure of credibility to any inference drawn present problems. Thus if the observations are perfectly correlated, the same conclusions are drawn from each variable; if the observations are completely independent and it is agreed to claim a difference at the 5% level if at least one variable shows significance, then one falsely claims significance with probability 1 (.95)n with n variables; if the rule is to claim a difference only if all variables show significance, then the