This chapter introduces the basic concepts of generalized linear models. To understand the concepts, knowledge about different data types, underlying assumptions regarding the data for use in linear models, assumptions of linear models, estimation procedures, test procedures, etc., are required. The theory of generalized linear model has rapidly grown to address unresolved issues concerning statistical models for both normal and non-normal outcome variables, since the publication of Wedderburn (1972) and then further developments by Nelder and Wedderburn, R. W. M. (Generalized linear models specified in terms of constraints. Journal of the Royal Statistical Society, Series B (Methodological), 36(3), 449–454.;Wedderburn, Biometrika 61:439–447, 1974b) and McCullagh and Nelder (Generalized linear models, Chapman and Hall, 1989). Generalized linear models provides a unified approach by a generalized framework for discrete, continuous, and categorical response variables. This chapter provides a detailed background along with their links with the development of a generalized linear model.