Logistic Regression Equation Logistic Regression

51

4.3. Logistic Regression

4.3.1. Logistic Regression Equation

The goal of logistic regression is to find the best fitting model to describe the relationship between the dichotomous characteristic of interest deforestation and the set of independent variable, they are: • LogR_Riv β 1 , distance from River 1 km • LogR_Road β 2 , distance from road 1 km • LogR_SL β 3 Shore LineCoast Line, distance from coast line 1 km • LogR_CP β 4 Center of Population, distance from sub_district office as a point center of population 10 km. • LogR_Com β 5 CompassAspect • LogR_SLP β 6 Slope • LogR_Alt β 7 Altitude, with topographic 250 and ≥ 250 meters Logistic regression generates the coefficients of equation to predict the transformation of the probability of occurrence of deforestation. An independent variable with a regression coefficient not significantly different 0 Sig. 0.05 can be removed from the regression model. If Sig. 0.05 means that the variables can contribute significantly to predict the outcomes variable. Table 4.1 is showing the distribution value of coefficients as the result of SPSS program process. In SPSS Program, there are some methods to select the way independent variables are entered into the model. Two of them are Forward and Stepwise. Forward is a method that entered significant variables sequentially, and Stepwise is method that entered variables sequentially; and after entering a variable in the model, checked and 52 possibly remove variables that become non-significant. In this study, it uses Forward Stepwise with Likelihood Ratio. Table 4.1 Variables not in the Equation Score df Sig. LOGR_RIV 53.245 1 .000 LOGR_ROA 93.857 1 .000 LOGR_SL 42.649 1 .000 LOGR_CP 54.188 1 .000 LOGR_COM 5.146 1 .023 LOGR_SLP 12.191 1 .000 Variables LOGR_ALT 2.964 1 .085 Step 0 Overall Statistics 230.667 7 .000 After removing the non-significant variables that Sig. is greater than 0.05, from Table 4.1, it can be seen that LogR_Alt altitude is non-significant variable to be entered further to the model. By introducing the entire sample data to the specific logistic regression model, in the first step, the variable of road distance 1,000 meters LogR_Road entered to the model Table 4.2. And the next variables will be entered by the program, and displayed by step by step and shown in Appendix III. Table 4.2 is only showing the sixth step, since this step will be used for analyzing. After the sixth step, there is no independent variable could enter to the model. This means that the only remained variable i.e altitude, could not cause any significant improvement to the performance of the model and its suitability with the data. This can be both because of the irrelevance of this variable to the phenomenon, and the altitude is relative to be flat area. The altitude is divided into ≥ 250 and 250 meters with portions 2581 pixels 23.4 and 8433 76.6. The altitude ≥ 250 meters is limited the highest area of this study area, but according to Supriatna, et al. 1994 in Tropenbos 1997 that altitude range of forest area in both Natural Reserve Cagar Alam Cibanteng and Wildlife Reserve Suaka Margasatwa Cikepuh 0 - 235 meters. Since the study area is relatively flat, with no wavy topography and with the ratio less or greater 250 meters, it must not be influenced to deforestation or stable occurrence. In this case deforestation or stable condition is not determined by the limitation altitude at least until 250 meters from sea level high. Table 4.2 . Variables in the equation a Variables entered on step 1: LOGR_ROA. β S.E. Wald df Sig. ExpB Step 6f LOGR_RIV -.232 .042 30.593 1 .000 .793 LOGR_ROA 1.130 .135 70.293 1 .000 3.097 LOGR_SL .348 .046 57.956 1 .000 1.416 LOGR_CP .354 .080 19.584 1 .000 1.425 LOGR_COM .082 .041 3.968 1 .046 1.086 LOGR_SLP -.150 .055 7.506 1 .006 .861 Constant -.238 .055 18.903 1 .000 .788 b Variables entered on step 2: LOGR_RIV. c Variables entered on step 3: LOGR_SL. d Variables entered on step 4: LOGR_CP. e Variables entered on step 5: LOGR_SLP. f Variables entered on step 6: LOGR_COM. The logistic regression equation becomes: Once an adequate model has been obtained, the next step is to test the significance of the parameters estimates. 53 54

4.3.2. Significance Test of Model and Predictors