Walter W. Stroup

Heritability ( H ) on a progeny mean basis is frequently estimated in recurrent selection experiments for the purpose of estimating the expected progress from family selection; however, appropriate measures of precision have been developed for only a few heritability estimators. The objective of this research was to develop a measure of precision for H for certain balanced linear models. Exact confidence intervals for H were derived and are not restricted to a specific experimental design. The confidence intervals were applied to sorghum [ Sorghum bicolor (L.) Moench] half‐sib family data.

Acknowledgments. Chapter 1. Introduction. Chapter 2. Regression. Chapter 3. Analysis of Variance for Balanced Data. Chapter 4. Analyzing Data with Random Effects. Chapter 5. Unbalanced Data Analysis: Basic Methods. Chapter 6. Understanding Linear Models Concepts. Chapter 7. Analysis of Covariance. Chapter 8. Repeated-Measures Analysis. Chapter 9. Multivariate Linear Models. Chapter 10. Generalized Linear Models. Chapter 11. Examples of Special Applications. References. Index.

Abstract The mixed model equations as presented by C. R. Henderson offers the base for a methodology that provides flexibility of fitting models with various fixed and random elements with the possible assumption of correlation among random effects. The advantage of teaching analysis of variance applications from this methodology is presented. Particular emphasis is placed upon the relationship between choice of estimable function and inference space.

With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.