Nested Linear Mixed Models with Repeated Measurement for Analyzing Telecommunication Products

Model Linier Campuran Tersarang dengan Pengukuran Berulang untuk Menganalisis Produk Telekomunikasi

Authors

  • Fardilla Rahmawati Department of Statistics, IPB University, Bogor, Indonesia
  • Khairil Anwar Notodiputro Department of Statistics, IPB University, Bogor, Indonesia
  • La Ode Abdul Rahman Department of Statistics, IPB University, Bogor, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v7i1p1-14

Keywords:

internet data quota, nested linear mixed model, repeated measurement

Abstract

Nested linear mixed model is a model that combines fixed factors and random factors. Observations made over time with the same object being observed are called repeated measurements. This research was conducted to determine the determinant factors of internet data quota sales which are influenced by SA (Sales Area), MC (Mutual Check), PC (Product Category), and time factors using a nested linear mixed model with repeated measurement. SA, PC, and time factors as fixed factors while the MC factor nested in SA as a random factor. The results showed that the interaction effect between three fixed factors, namely between SA, PC, and time have a significant effect on the sales volume of internet data quota. Moreover, variation in the sales volume between MC factors was significant. The interaction between MC and PC, and the interaction between MC and time were significant on the sales volume of internet data quota.

Downloads

Download data is not yet available.

References

Aunuddin. (2005). Statistika: Rancangan dan Analisis Data. Bogor (ID): IPB Press.

Coull, B. A., Schwartz, J., & Wand, M. P. (2001). Respiratory health and air pollution: additive mixed model analyses. Biostatistics, 2(3): 337–349.

Duan, X. De, Zhang, S., Zhang, W. Z., & Wu, Y. (2019). Nested Inverse Gaussian Mixed-Effects Model for Longitudinal Data. Procedia Computer Science, 154: 561–565.

Mattjik, A. A., & Sumertajaya, I. M. (2000). Perancangan Percobaan dengan Aplikasi SAS dan Minitab Jilid I Edisi Kedua. Bogor (ID): IPB Press.

Montgomery, D. C. (2009). Introduction to Statistical Quality Control, Sixth Edition. Danvers (US): John Wiley & Sons, Inc.

Montgomery, D. C. (2012). Design and Analysis of Experiments Eighth Edition. Arizona (US): John Wiley & Sons.

Moscatelli, A., Mezzetti, M., & Lacquaniti, F. (2012). Modeling psychophysical data at the population-level: The generalized linear mixed model. Journal of Vision, 12(11): 1–17.

Nirmala, F., Kuntoro, & Notobroto, H. B. (2013). Aplikasi General Linear Mixed Model(GLMM) pada Data Longitudinal Kadar Trombosit Demam Berdarah Dengue. Jurnal Biometrika Dan Kependudukan, 2(2): 131–139.

Searle, S. R., Casella, G., & McCulloch, C. E. (1992). Variance Components. New York (US): John Wiley & Sons, Inc.

Stroup, W. W. (2013). Generalized Linear Mixed Models. Boca Raton (US): Taylor & Francis Group, LLC.

Szyszkowicz, M. (2006). Use of generalized linear mixed models to examine the association between air pollution and health outcomes. International Journal of Occupational Medicine and Environmental Health, 19(4): 224–227.

Tantular, B. (2012). Pendekatan Model Multilevel untuk Data Repeated Mesures. Prosiding Matematika FMIPA UNY, (November), 978–979.

Tenaya, I. M. N. (2015). Pengaruh Interaksi dan Nilai Interaksi pada Percobaan Faktorial (Review). Journal on Agriculture Science, 5(1): 9–20.

Downloads

Published

31-10-2023

How to Cite

Rahmawati, F., Notodiputro, K. A., & Rahman, L. O. A. (2023). Nested Linear Mixed Models with Repeated Measurement for Analyzing Telecommunication Products: Model Linier Campuran Tersarang dengan Pengukuran Berulang untuk Menganalisis Produk Telekomunikasi. Indonesian Journal of Statistics and Its Applications, 7(1), 1–14. https://doi.org/10.29244/ijsa.v7i1p1-14

Issue

Section

Articles

Most read articles by the same author(s)

1 2 > >>