The Common Errors in Statistical Modelling and Forecasting

By Hassan Doosti.

Published by The Organization Collection

Format Price
Article: Print $US10.00
Article: Electronic $US5.00

Regression analysis is a powerful tools which are used in many areas of management research. Usually, in using ordinary regression models some basic assumptions should be hold which ignoring to check situations of them are com¬mon errors in statistical modeling and forecasting.After reviewing this kind of common error, we suggest modern techniques which may help the researchers to overcome the problem as an alternative models. Key words: Ordinary regression model, Nonlinear Regression, Generalized linear model, Generalized autoregressive conditional heteroskedasticity (GARCH), Nonparametric regression, Robust Regression

Keywords: Ordinary Regression Model, Nonlinear Regression, Generalized Linear Model, Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Nonparametric Regression, Robust Regression

International Journal of Knowledge, Culture and Change Management, Volume 10, Issue 11, pp.107-110. Article: Print (Spiral Bound). Article: Electronic (PDF File; 678.878KB).

Dr. Hassan Doosti

Assistant Professor, Department of Statistics, Tarbiat Moallem University, Mashhad, Tehran, Iran (Islamic Republic of)

He is assistant professor at department of Mathematics, Tarbiat Moallem University, Tehran, Iran.


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