Linear model (LM) provide the advance in regression analysis, where it was consideres an important development of the last 45 years, following general linear model (GLM), principal component analysis (PCA) and constrained principal component analysis (CPCA) in the last 25 years. This paper introduce a series of papers perpared within the framework of an international workshop. Firstly the LM and GLM has been discusses.  Next , and overview of  PCA has been presented. Then constrained principal component has been shown. Som of its special cases, related methods and ordinary least squares OLS estimator as a special case form CPCA has been introduced . finally , an example has been introduced to indicate the importance of CPCA and the different between PCA and CPCA.