PENGGUNAAN MODEL REGRESI LINIER UNTUK MENYATAKAN HUBUNGAN FUNGSIONAL PERUBAHAN KONSENTRASI OKSIGEN TERLARUT TERHADAP PARAMETER FISIKA-KIMIA AIR SUNGAI SECANG KULON PROGO - , Mukti Ali Imran ([Yogyakarta] : Universitas Gadjah Mada, 2013)
The aplication of linear regression models (LRM) has been performed to express functional relationships change of dissolved oxygen concentration on the physico-chemical parameters of Kulon Progos Secang river water. LRM derived from linear regression analysis using software tools package SPSS 17.0. Changes in water quality watershed/river Secang by changes in the physico-chemical parameters mainly due to the erosion and sedimentation processes of sloping land activities in the upstream area. Concentration of Dissolved Oxygen Dissolved Oxygen (DO), is one of the physico-chemical parameters of water importantly, as an indicator of water quality. The abundance of suspended material primarily from sedimentation processes, one of which will result in an increase in the surface temperature of the water, and therefore will decrease the concentration of dissolved oxygen. Various attempts of scientific approach have been made to predict changes in DO concentration, one of which is the mathematical modeling of multiple linear regression analysis techniques. This study aims to determine whether analysis and multiple regression models can be used to express functional relationships DO concentration changes to changes in the physico-chemical parameters T, TSS, and Dh, of Secang river. Multiple linear regression analysis (MLRA) has been done involving the dependent variable is the DO, and three independent variables, namely T, TSS, and depth (Dh). Preparation of model framework was conducted using the Enter (E), Stepwise (S), and Beckward (B), as well as combinations of the three methods. Total regression models obtained are ten, eight models of ARLB stage one, and each one of the models ARLB stage two and three. After a simultaneous test, partial test, and classical assumption multikolinieritas for acceptance and feasibility of the model, it was concluded bahawa multiple linear regression model in stage two and stage three is expressed as a linear regression model that does not DO bias. The model reflects the functional relationship between the variables involved in the form of a causal relationship. It can be concluded that: analysis and multiple linear regression model can be used to express a functional relationship changes in the concentration of dissolved oxygen-DO to changes of physico-chemial parameters of T, TSS, and Dh, of Secang river.