A Comparison Study of Linear and Nonlinear Regression Models

Taghreed Abdul- Razek Abdul-Motaleb Al-Said

Taghreed Abdul- Razek Abdul-Motaleb Al-Said A lecturer of Statistics at Al Azhar University, Faculty of Commerce (Womens' Branch), Department of Statistics, Assistant professor of Statistics at King Abdul-Aziz University, Egypt

Keywords: Linear regression models, logistic regression models, ordinary least-square, Wald test, R-squared test.

Abstract

Regression analysis is an important statistical tool for analyzing the relationships between dependent, and independent variables. The main goal of regression analysis is determine, and estimate parameters of a function that describe the best fit for a given data sets. There are many linear types of regression analysis models such as simple and multiple regression models. Also, there are the non-linear regression analyses such as binary and multinomial logistic regression models. This research at first, introduced many types of such models. Second, estimates the parameters of the models by using the maximum likelihood estimation, and the least square estimation methods. Also, it introduces some criteria for evaluating methods. Two suitable applications on two different data sets are conducted, and useful results are concluded.

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