THE APPLICATION OF WEIGHTED LEAST SQUARE (WLS) IN PARAMETER ESTIMATION OF A PATH ANALYSIS
DOI:
https://doi.org/10.61841/vvgyxj21Keywords:
Path Analysis, Multivariate Analysis, WLS, Vertical Garden.Abstract
---Path analysis is one of the multivariate analysis techniques and development of regression analysis developed by Sewall Wright in 1934. The vertical garden technique is one of the greening techniques that can be applied in green building. Vertical garden is an upright garden concept, namely plants and other garden elements arranged in such a way in an upright field. In this study the author wants to find out what factors influence the interest of the people of Bendosari Village in building a vertical garden. The data used in this study are primary data with respondents from the Bendosari Village community from Cukal, Dadapan Wetan, Dadapan Kulon, Ngprih and Tretes. The method used is path analysis with the Weighted Least Square (WLS) method as an estimator of its parameters. The WLS method is used to overcome (heterogeneous) error constants in the data obtained. The results of this study indicate that the attitudes, intentions and behavior of the Bendosari Village community in constructing vertical gardens are significantly influenced by Perception of Benefits, Influence of Social Environment and Motivation. Whereas the Perception of Ease of Development does not affect the attitudes, intentions, and behavior of the Bendosari Village community in building a vertical garden significantly. This shows that the Bendosari Village community is a reliable and competent community because the Bendosari Village community tends to ignore the view that whether a facility is easy or difficult to make before building it.
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References
[1] Dillon, W. R. dan Goldstein, M. (1984). Multivariate Analysis Methods and Application. New York: John Wiley Sons. Inc.
[2] Fernandes, A.A.R.,Nyoman Budiantara, I.,Otok, B.W.,Suhartono, (2014), “Reproducing Kernel Hilbert space and penalized weighted least square in nonparametric regression”, Applied Mathematical Sciences, Vol 8 No 146, pp 7289-7300
[3] Mulyaningsih, Y. (2015). Makalah Arsitektur Lansekap Vertical Garden. Bogor: Universitas Djuanda.
[4] Fernandes, A.A.R, Budiantara, I.N, Otok, B.W., and Suhartono. (2015). “Spline Estimator for Bi-Responses and Multi-Predictors Nonparametric Regression Model in Case of Longitudinal Data”, Journal of Mathematics and Statistics, Vol 11, No 2, pp. 61-69.
[5] Wang, Y. (2011). Smoothing Spline Methods Applications. New York: CRC Press.
[6] Gudono. (2012). Analisis Data Multivariat. Edisi Kedua. Yogyakarta: BPFE.
[7] Solimun, Fernandes, A.A.R. (2017). “Investigation the mediating variable: What Necessary? (Case Study in Management Research)”, International Journal of Law and Management, Vol 59 No 6, pp 101-110.
[8] Mendenhall, S. (1996). A Second Course In Statistics. Regression Analysis. Fifth Edition. Prentice Hall Internatiomal Edition.
[9] Fernandes, A.A.R., Hutahayan, B., Solimun, Arisoesilaningsih, E., Yanti, I., Astuti, A.B., Nurjannah, & Amaliana, L, (2019), “Comparison of Curve Estimation of the Smoothing Spline Nonparametric Function Path Based on PLS and PWLS In Various Levels of Heteroscedasticity”, IOP Conference Series: Materials Science and Engineering, Forthcoming Issue.
[10] Susanti, Yuliana. (2014). M Estimation, S Estimation, and MM Estimation In Robust Regression. Surakarta: Universitas Sebelas Maret.
[11] Hutahayan, B., Solimun, Fernandes, A.A.R., Arisoesilaningsih, E., Yanti, I., Astuti, A.B., Nurjannah, & Amaliana, L, (2019), “Mixed Second Order Indicator Model: The First Order Using Principal Component Analysis and The
Second Order Using Factor Analysis”, IOP Conference Series: Materials Science and Engineering, Forthcoming Issue.
[12] Gujarati, D. (2004). Basic Econometrics. Fourth Edition. New York: McGraw Hill.
[13] Wu, H. dan Zhang, J.T. (2006). Nonparametric Regression Methods for Longitudinal Data Analysis. New Jersey: John Wiley and Sons. Inc.
[14] Sheskin, D.J. (2000). Parametric and Nonparametric Statistical Procedures. Second Edition.New York: CRC Press
[15] Kutner, M.H., Nachtsheim, C.J., Neter, J. dan Li W. (2005). Applied Linear Statistical Models. Fifth Edition. Boston: McGraw-Hill International.
[16] Djarwanto PS dan Pangestu Subagyo. (2011). Statistik Induktif. Edisi 4. Yogyakarta: Penerbit BPFE.
[17] Fernandes, A.A.R, Jansen, P., Sa’adah, U, Solimun, Nurdjannah, Amaliana, L., and Efendi, A. (2018). “Comparison of Spline Estimator at Various Levels of Autocorrelation in Smoothing Spline Nonparametric Regression For Longitudinal Data”, Communications in Statistics – Theory and Methods, Vol 46, No 24, pp 12401- 12424.
[18] Mendenhall, W., Beaver, R.J. dan Beaver, B.M. (2009). Introduction to Probability and Statistics, Thirteenth edition. USA. Brooks/Cole Cengage Learning.
[19] Solimun. (2010). Analisis Multivariat Pemodelan Struktural Metode Partial Least Square-PLS. Malang: CV. Citra Malang.
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