A Comparative Study of Estimation Methods for Parameters in Multiple Linear Regression Model

• Published in: Journal of Applied Animal Research Vol. 29; no. 1; pp. 43 - 47
• Main Authors: Cankaya, Soner, Kayaalp, G. Tamer, Sangun, Levent, Tahtali, Yalcin, Akar, Mustafa
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 01.03.2006
• Subjects: least median squares; Least squares; least trimmed squares; non-parametric multiple linear regression; robust regression; Serranus cabrilla
• ISSN: 0971-2119; 0974-1844
• DOI: 10.1080/09712119.2006.9706568

Cankaya, S., Kayaalp, G.T., Sangun, L., Tahtali, Y. and Akar, M. 2006. A comparative study of estimation methods for parameters in multiple linear regression model. J. Appl. Anim. Res., 29: 43-47. This paper investigated least squares method, non-parametric method and robust regression methods to predict the parameters of multiple regression models. To evaluate these methods, measurements of body weight, total length and fork length of fishes collected from Serranus cabrilla were used. In these regression models, body weight was dependent variable whereas total length and fork length were independent variables. The results show that non-parametric regression method, general additive model, has minimum R2 value and least median squares has maximum R 2 value, 0.334 and 0.855, respectively.