Circular(2)-linear regression analysis with iteration order manipulation
(1) Muhamad Irpan Nurhab Mail (Department of Economics, STIE MURA, Indonesia)
(2) Badaruddin Nurhab Mail (Department of Economics, IAIN Bengkulu, Indonesia)
(3) * Tuti Purwaningsih Mail (Universitas Islam Indonesia, Indonesia)
(4) Ming Foey Teng Mail (American University of Middle East, Kuwait)
*corresponding author

Abstract


Data in the form of time cycle or point position to the angle of possibility is no longer suitable to be analyzed using classical linear statistic method because the direction and the angle influence the position between one data with other data. This paper aims to examine the comparison of Linear Regression Analysis with Circular Regression Analysis. The writing method used is literature review using simulation data. Data simulation and analysis is done with the help of R program. The results showed that circular data is better analyzed by Circular Regression Analysis rather than Classical Linear Regression Analysis. The use of classical linear statistic method is not recommended due to the direction and the angle influence the position between one data with other data.

Keywords


data circular; circular; linear regression

   

DOI

https://doi.org/10.26555/ijain.v3i2.90 		      

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References


S. R. Jammalamadaka and A. Sengupta, Topics in circular statistics. River Edge, N.J: World Scientific, 2001.

P. E. Jupp and K. V Mardia, “A general correlation coefficient for directional data and related regression problems,” Biometrika, vol. 67, no. 1, pp. 163–173, 1980.

C. Brunsdon and J. Corcoran, “Using circular statistics to analyse time patterns in crime incidence,” Comput. Environ. Urban Syst., vol. 30, no. 3, pp. 300–319, 2006.

M. I. Nurhab, A. Kurnia, and I. M. Sumertajaya, “Circular Circular–Linear Regression Analysis of Order m in Circular Variable α and β against Linear Variable (Y).”

N. I. Fisher, Statistical Analysis of Circular Data, 3rd ed. New York: Cambridge University Press, 1995.

D. A. Freedman, Statistical models: theory and practice. cambridge university press, 2009.

M. Krzywinski and N. Altman, “Points of Significance: Multiple linear regression,” Nat. Methods, vol. 12, no. 12, pp. 1103–1104, 2015.

Jammalamadaka, S. Rao and Y. R. Sarma, Statistical Theory and Data Analysis II: Proceedings of the Second Pacific Area Statistical Conference, 2nd ed. North Holland: Elsevier Science Ltd, 1988.

A. H. Abuzaid, I. B. Mohamed, and A. G. Hussin, “Procedures for outlier detection in circular time series models,” Environ. Ecol. Stat., vol. 21, no. 4, pp. 793–809, Dec. 2014.

S. Kim and A. SenGupta, “Inverse Circular--Linear/Linear--Circular Regression,” Commun. Stat. Methods, vol. 44, no. 22, pp. 4772–4782, 2015.

M. Linder and M. Williander, “Circular business model innovation: inherent uncertainties,” Bus. Strateg. Environ., vol. 26, no. 2, pp. 182–196, 2017.

M. Di Marzio, A. Panzera, and C. C. Taylor, “Nonparametric circular quantile regression,” J. Stat. Plan. Inference, vol. 170, pp. 1–14, 2016.

P. Guerrero and J. R. del Solar, “Circular Regression Based on Gaussian Processes,” in Pattern Recognition (ICPR), 2014 22nd International Conference on, 2014, pp. 3672–3677.

T. Peiris and S. Kim, “Restricted Inference in Circular-Linear and Linear-Circular Regression,” Sri Lankan J. Appl. Stat., vol. 17, no. 1, 2016.

M. B. Miles, A. M. Huberman, and J. Saldana, Qualitative data analysis. Sage, 2013.

M. Oliveira Pérez, R. M. Crujeiras Casais, and A. Rodr’iguez Casal, “NPCirc: An R package for nonparametric circular methods,” 2014.

A. Rambli, A. H. M. Abuzaid, I. Bin Mohamed, and A. G. Hussin, “Procedure for Detecting Outliers in a Circular Regression Model,” PLoS One, vol. 11, no. 4, p. e0153074, 2016.




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