PERBANDINGAN REGRESI LOGISTIK BINER DAN PROBIT BINER DALAM PEMODELAN TINGKAT PARTISIPASI ANGKATAN KERJA

Hendra H. Dukalang

Abstract


Regression is a data analysis method used to model the relationship between one response variable and one or more predictor variables. In regression modelling, data is often used. In general, the regression model that is often used is simple or multiple regression in modelling where the response variable is quantitative data. The fundamental difference from regression models using quantitative data is the main objective is to estimate the average value of the dependent variable using certain values of the independent variable. Whereas in a regression model with a qualitative dependent variable the main objective is to find the probability of something happening (probability model). One of the development methods of the regression model for data with qualitative response variables is Logistic and Probit regression. The purpose of this study was to compare the best model using binary logistic regression with binary probit regression in the case of Labor Force Participation Rate (TPAK) in Gorontalo City. The research method used is quantitative research methods, with binary logistic regression modelling and binary probit regression. The results showed that the variable that has a significant effect on TPAK Gorontalo City is the open unemployment rate, and the best model between the binary logistic regression model with an AIC value of 1.289 is smaller than the AIC value of the binary Probit regression 1.318, likewise from the R2 value the R2 value for regression is obtained. binary logistic of 12.74%, greater than the R2 value of binary probit regression of 10.70%.

Keywords


Regression; Binary Logistics; Binary Probit; TPAK

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References


D.W. Hosmer, S. Lemeshow, R.X. Sturdivant, Applied logistic regression, 3rd Edition. John Wiley & Sons, 2013.

A. Agresti, Categorical data analysis, 3rd Edition. John Wiley & Sons, 2013.

D.J. Finney, Probit analysis, 3rd Edition. Cambridge University Press, 1971

Gujarati, Damodar. Dasar-dasar Ekonometrika, Edisi Kelima. Mangunsong, R. C. penerjemah. Jakarta: Salemba Empat. 2013

S. Hosseinian, E. Martinez, Robust binary regression. Journal of Statistical Planning and Inference, 141, 1497-1509, 2011

A.M. Bianco, E. Martinez, Robust testing in the logistic regression model. Computational Statistics and Data Analysis, 53, 4095-4105, 2009.

D. Pregibon, Logistic regression diagnostics. The Annals of Statistics, 9, 705-724, 1981.

D.W. Hosmer, B. Jovanovic, S. Lemeshow, Best subsets logistic regression. Biometrics, 45, 1265-1270, 1989.

T. Kliestik, K. Kocisova, M. Misankova, Logit and probit model used for prediction of financial health of company. Procedia Economics and Finance, 23, 850-855, 2015.

K.B. Karlson, A. Holm, R. Breen, Comparing regression coefficients between same-sample nested models using logit and probit: a new method. Sociological Methodology, 42, 286-313, 2012.

M. Razzaghi, The probit link function in generalized linear models for data mining applications. Journal of Modern Applied Statistical Methods, 12, 164169, 2013.

Permatasari, D.L. dan Ratnasaril V. Pemodelan Ketahanan Pangan di Indonesia dengan Pendekatan Regresi Probit Ordinal. Jurnal Sains dan Seni ITS, Vol.5, No.2, Hal:151-156. 2016.




DOI: https://doi.org/10.34312/euler.v7i2.10355

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