BibTex Citation Data :
@article{Medstat2465, author = {Hasbi Yasin}, title = {PEMILIHAN VARIABEL PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION}, journal = {MEDIA STATISTIKA}, volume = {4}, number = {2}, year = {2011}, keywords = {}, abstract = { Regression analysis is a statistical analysis that aims to model the relationship between response variable with some predictor variables. Geographically Weighted Regression (GWR) is statistical method used for analyzed the spatial data in local form of regression. One of the problems in GWR is how to choose the significant variables. The number of predictor variables will allow the violation of assumptions about the absence of multicollinearity in the data. Therefore, this needs a method to reduce some of the predictor variables which not significant to the response variable. This paper will discuss how to select significant variables by stepwise method. This method is a combination of forward selection method and the backward elimination method. Keywords : Geographically Weighted Regression, Backward Elimination, Forward Selection, Stepwise Method }, issn = {2477-0647}, pages = {63--72} doi = {10.14710/medstat.4.2.63-72}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2465} }
Refworks Citation Data :
Regression analysis is a statistical analysis that aims to model the relationship between response variable with some predictor variables. Geographically Weighted Regression (GWR) is statistical method used for analyzed the spatial data in local form of regression. One of the problems in GWR is how to choose the significant variables. The number of predictor variables will allow the violation of assumptions about the absence of multicollinearity in the data. Therefore, this needs a method to reduce some of the predictor variables which not significant to the response variable. This paper will discuss how to select significant variables by stepwise method. This method is a combination of forward selection method and the backward elimination method.
Keywords: Geographically Weighted Regression, Backward Elimination, Forward Selection, Stepwise Method
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