The univariate, multivariate, split-plot in time, and covariance model approaches will be discussed. Each factor in an analysis of variance model can be described as a random or fixed effect. Random effects are used when a description of variability among levels, rather than comparisons between levels, is appropriate. For example, in a plant breeding trial, several female and male plants may be selected at random from a larger population of plants. Variability among their crosses may then be expressed in terms of the variances among females and among males.
Variance components are also useful in power and sample size calculations when planning experiments.
This workshop will include a comparison of least-squares, maximum likelihood, and minimum variance quadratic unbiased methods of estimation. The assumptions used to make inferences in ordinary linear regression are that the residuals are independent of each other and are normally distributed with a mean of zero and constant variance. Violations of these assumptions could cause the results to be misleading. In this workshop, you will learn how to assess the goodness of fit of a regression model with diagnostic and influence plots. Also included will be added variable and partial residual plots, which are helpful in determining whether additional predictors are useful in a multiple regression model.