ABSTRACT
One can look at statistical analysis of data as a medium “for extracting information from observed data and dealing with uncertainty” (Rao, 1989, p. 98).
Another way of saying the same thing is to consider
statistics
as a group of methods that are used to collect, analyze, present, and interpret data. From the myriad of methods available to us for data analysis (Snedecor and Cochran, 1980; Spanos, 1999), regression methods are one family of data analysis that are comprehensive in their ability to address data issues, efficient in their ability to extract large amounts of information in a concise way, and widely available, because even spreadsheet software provides for facilities to estimate simple regression models. Regression methods, particularly when one considers generalized regression models, can also be considered as the general family of models that contains analysis of variance and a variety of other methods for the analysis of experiments as special cases. Regression methods and models are also the techniques dominating
econometrics —
the art and science of analyzing economic data, which, when considering the leading textbooks on the subject, is nothing but the study of regression models (see Amemiya, 1985; Greene, 2000; Johnston and DiNardo, 1997; Pindyck and Rubinfeld, 1998; among many others).
