Using ridge regression to analysis the meteorological data in sulaimani

• Published in: المجلة العراقية للعلوم الاحصائية Vol. 17; no. 2; pp. 76 - 86
• Main Author: Layla Ahmed
• Format: Journal Article
• Language: Arabic
• Published: College of Computer Science and Mathematics, University of Mosul 01-12-2020
• Subjects: meteorological variables; multicollinearity problem; ordinary least squares; ridge regression
• ISSN: 1680-855X; 2664-2956
• DOI: 10.33899/iqjoss.2020.167390

Linear regression is one of the frequently used statistical methods that have applications in all field of daily life. In a statistical perspective, the regression analysis is used for studying the relationship between a dependent variable and a set of independent variables. The ridge regression is the most widely model in solving the multicolinearity problem, and it'''s an alternative to OLS.Multicollinearity is the most common problem in multiple regression models in which there exists a perfect relationship between two explanatory variables or more in the model. In this study, ridge regression model was used to estimate linear regression model. This result was compared with result obtained using ordinary least squares model in order to find the best regression model. We have used meteorological data in this study. The results showed that the ridge regression method can be used to resolve the multicollinearity problem, without deleting the independent correlated variables of the model and able to estimate parameters with lower standard error values.