This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book considers a class of statistical models that is a natural generalization of classical linear models. Generalized linear models include as special cases, linear regression and analysis-of-variance models, logit and probit models for quantal responses, log-linear models and multinomial response models for counts and some commonly used models for survival data. Classical linear models and least squares began with the work of Gauss and Legendre who applied the method to astronomical data. The book deals with the origin of generalized linear models, describing various special cases that are now included in the class in approximately their chronological order of development. The development of the theory of experimental design gave a new stimulus to linear models and is very much associated with R. A. Fisher and his co-workers.