The Classical Assumption Test to Driving Factors of Land Cover Change in the Development Region of Northern Part of West Java

Abstract

ABSTRACT:Land cover changes continuously change by the time. Many kind of phenomena is a simple of important factors that affect the environment change, both locally and also globally. To determine the existence of the phenomenon of land cover change in a region, it is necessary to identify the driving factors that can cause land cover change. The relation between driving factors and response variables can be evaluated by using regression analysis techniques. In this case, land cover change is a dichotomous phenomenon (binary). The BLR's model (Binary Logistic Regression) is the one of kind regression analysis which can be used to describe the nature of dichotomy. Before performing regression analysis, correlation analysis is carried it the first. Both correlation test and regression tests are part of a statistical test or known classical assumption test. From result of classical assumption test, then can be seen that the data used to perform analysis from driving factors of the land cover changes is proper with used by BLR's method. Therefore, the objective of this research is to evaluate the effectiveness of methods in assessing the relation between driving factors of land cover change that assumed can affect to land cover change phenomena. This research will use the classical assumed test of multiple regression linear analysis, showing that BLR method is efficiency and effectiveness solution for researching or studying in phenomenon of land cover changes. So it will to provide certainty that the regression equation obtained has accuracy in estimation, unbiased and consistent.

Factors Affecting the Performance of F&B Industry in Malaysia and Indonesia

“…Then, in terms of autocorrelation test for both country's F&B industry data, the Durbin-Watson (DW) values are between 1 and 3, so then it is able to interpret that there is no issue of autocorrelation in this research. And then, in terms of heteroscedasticity test, the probability value of heteroscedasticity likelihood ratio for both Malaysia's and Indonesia's F&B industry data are more than 5% of significance level in which the null hypothesis will be accepted where all data of research regression model is free from heteroscedasticity problem (Ainiyah, et al, 2016). Note: ***, **, * denotes significant at 1%, 5%, and 10% level.…”

“…This regression was applied due to the use of more than one predictive variable influencing the dependent variable of which factors give the most impact. Secondly, it proceeds to the classical assumption tests which consist of normality test to value if the research residuals data are normally distributed by assessing the probability of Jarque-Bera in the regression model, multicollinearity test to value if correlation among the independent variables has existed by assessing the values of correlation between two variables within the multicollinearity matrix table, autocorrelation test to show similarity degree between values of same variables by assessing the standard value of Durbin-Watson (DW) in the regression model, and heteroscedasticity test to assess if there is an inequality of variance of the residuals for all observations in the linear regression model by assessing the probability value of heteroscedasticity likelihood ratio (Ainiyah et al, 2016). In addition, the research data was not transformed into the Log Natural (LN) with the consideration where it potentially passed the heteroscedasticity test (Yobero, 2016).…”

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“…In this study, the researcher uses logistic regression models because the response of the dependent variables are dichotomous or categorical (Barbić et al, 2016). Dichotomous or categorical means that the data only have two possible outcomes, either success that usually denoted as "1" and unsuccess usually denoted as "0" (Ainiyah et al, 2016).…”

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“…According to Ainiyah, Deliar, and Virtriana (2016), heteroscedasticity occurs when the residual variances of a regression model are not equal or constant across observations. The regression model is considered good if there is no heteroscedasticity.…”