Despite its many origins in agronomic problems, statistics today is often unrecognizable in this context. Numerous recent methodological approaches and advances originated in other subject-matter areas and agronomists frequently find it difficult to see their immediate relation to questions that their disciplines raise. On the other hand, statistic
Statistical Models. Data Structures. Linear Algebra Tools. The Classical Linear Model: Least Squares and Alternatives. Nonlinear Models. Generalized Linear Models. Linear Mixed Models for Clustered Data. Nonlinear Models for Clustered Data. Statistical Models for Spatial Data. Bibliography.