In this present book Chapter I is an introductory one. It contains the general introduction about the importance of hypotheses testing in econometrics. Chapter II deals with the inferential aspects of linear models. It describes the various problems of the theory of Econometrics. Chapter III describes the existing criteria for testing general linear hypotheses in the linear models. It contains the derivation and applications of Restricted Least Squares estimation in the theory of Econometrics.Chapter IV proposes same alternative criteria for testing general linear hypotheses in the generalized linear models. Mean Squared Error (MSE) criteria have been explained for testing general linear hypotheses in the generalized linear models under the problems of heteroscedasticity and singular linear models.Chapter V gives the conclusions of the book .Several relavant articles regarding the Hypotheses testing in linear regression models have been presented under a title ‘BIBLIOGRAPHY’
This book points the attention on a very crucial topic in Statistics - Model Selection - from a Bayesian point of view. In particular we are interested in analyzing the way in which we have to think and rationalize, when dealing with a problem of model choice. In the Classical background, this problem is strictly related to the field of hypotheses testing, since most of the tools used by Classical statisticians, to support such type of decision, are tests over parameters in the model or likelihood ratios. In spite of this, Bayesian theory allows us to tackle this problem in a more general setting that does not necessarily coincide with the hypotheses testing approach, leading us to the point that a threshold between these two settings is needed. However it is not clear yet where the hypotheses testing ends, and the model selection begins. A possible key to the solution of this matter lies on the definition of a statistical model, and more specifically of a nested model. A model selection problem with nested models identified by inequality constraints will be considered to illustrate this idea, with the support of an application implemented with Matlab.
I explored the relationship between university students’ cheating behaviors and their cognitive development levels, use of neutralization techniques, self-concept as a multifaceted cognitive construct, and attitude toward cheating. The study was a correlation design that required survey methods and tested 5 hypotheses. The data supported the first, third, and fourth hypotheses. However, the second and fifth hypotheses were supported only under certain conditions. The roles of cognitive development and self-concept in academic dishonesty represented major findings.