Least squares methods is an essential statistical technique in many areas including the life sciences, environmental sciences, and agriculture. This book provides the background necssary to understand the important concepts of regression analysis without becoming excessively mathematical.

Inhalt
Review of Simple Regression.- to Matrices.- Multiple Regression in Matrix Notation.- Analysis of Variance and Quadratic Forms.- Case Study: Five Independent Variables.- Geometry of Least Squares.- Model Development: Variable Selection.- Polynomial Regression.- Class Variables in Regression.- Problem Areas in Least Squares.- Regression Diagnostics.- Transformation of Variables.- Collinearity.- Case Study: Collinearity Problems.- Models Nonlinear in the Parameters.- Case Study: Response Curve Modeling.- Analysis of Unbalanced Data.- Mixed Effects Models.- Case Study: Analysis of Unbalanced Data.