Principal Component Analysis

approach would provide a natural way to properly account for uncertainty in the Ž xed effects in mixed models, due to the uncertainty in the parameters of the random-effects covariance matrix, and in prediction in spatial models due to the uncertainty in the estimation of the variogram parameters. Overall, Contemporary Statistical Models for the Plant and Soil Sciences is a solid text with many real-world examples/applications. It could be used for a second or third methods course for nonstatistics graduate students, or as a text for the second semester of a two-semester methods sequence for statistics masters students. The authors should be commended for providing a comprehensive and modern methods textbook for researchers in the agricultural sciences.