Open AccessPemodelan Tingkat Penghunian Kamar Hotel di Kendari dengan Transformasi Wavelet Kontinu dan Partial Least Squares
Author(s) -
Margaretha Ohyver,
Herena Pudjihastuti
Publication year - 2014
Publication title -
comtech/comtech
Language(s) - English
Resource type - Journals
eISSN - 2476-907X
pISSN - 2087-1244
DOI - 10.21512/comtech.v5i2.2435
Subject(s) - multicollinearity , outlier , statistics , linear regression , regression analysis , ordinary least squares , wavelet , variables , partial least squares regression , mathematics , regression , computer science , econometrics , artificial intelligence
Multicollinearity and outliers are the common problems when estimating regression model. Multicollinearitiy occurs when there are high correlations among predictor variables, leading to difficulties in separating the effects of each independent variable on the response variable. While, if outliers are present in the data to be analyzed, then the assumption of normality in the regression will be violated and the results of the analysis may be incorrect or misleading. Both of these cases occurred in the data on room occupancy rate of hotels in Kendari. The purpose of this study is to find a model for the data that is free of multicollinearity and outliers and to determine the factors that affect the level of room occupancy hotels in Kendari. The method used is Continuous Wavelet Transformation and Partial Least Squares. The result of this research is a regression model that is free of multicollinearity and a pattern of data that resolved the present of outliers.