Pal M., Bharati P. Applications of Regression Techniques

Springer, 2019. - 180 p. - ISBN: 9811393133.This book discusses the need to carefully and prudently apply various regression techniques in order to obtain the full benefits. It also describes some of the techniques developed and used by the authors, presenting their innovative ideas regarding the formulation and estimation of regression decomposition models, hidden Markov chain, and the contribution of regressors in the set-theoretic approach, calorie poverty rate, and aggregate growth rate. Each of these techniques has applications that address a number of unanswered questions; for example, regression decomposition techniques reveal intra-household gender inequalities of consumption, intra-household allocation of resources and adult equivalent scales, while Hidden Markov chain models can forecast the results of future elections. Most of these procedures are presented using real-world data, and the techniques can be applied in other similar situations. Showing how difficult questions can be answered by developing simple models with simple interpretation of parameters, the book is a valuable resource for students and researchers in the field of model building.Introduction to Correlation and Linear Regression Analysis.
Regression Decomposition Technique Toward Finding Intra-household Gender Bias of Calorie Consumption.
Estimation of Poverty Rates by Calorie Decomposition Method.
Estimating Calorie Poverty Rates Through Regression.
Prediction of Voting Pattern.
Finding Aggregate Growth Rate Using Regression Technique.
Testing Linear Restrictions of Parameters in Regression Analysis.
The Regression Models with Dummy Explanatory Variables.
Relative Contribution of Regressors.True PDF