Productivity Prediction of Garment Employees using Multiple Linear Regression
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DOI:
https://doi.org/10.59287/ijanser.644Keywords:
Multiple Linear Regression, Multicollinearity, Variable, Statistical, TransformationAbstract
This paper presents a multiple linear curve fitting method for finding how the change of manipulated variables affect the final result using productivity prediction of garment employees data set. To perform fitting, a function is defined, which depends on the parameters that measures the closeness between the data and model. The simplest form of modelling is linear regression, which is the prediction of one variable from another. When the relationship between a few variables are assumed to be linear, then multiple linear regression will be used to model the relationship between a continuous response variable and continuous or categorical explanatory variables.
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References
Bangdiwala, S. I. (2018). Regression: simple linear. International Journal of Injury Control and Safety Promotion, 25(1), 113-115.
Escanciano, J. C. (2018). A simple and robust estimator for linear regression models with strictly exogenous instruments. The Econometrics Journal, 21(1), 36-54.
Goh, Y. L., & Pooi, A. H. (2012). Confidence intervals for multivariate value at risk. Scienceasia, 39S, 70-74.
Laverty, W. H., & Kelly, I. W. (2021). Exploring the effects of assumption violations on simple linear regression and correlation using excel. American Journal of Theoretical and Applied Statistics, 10(4), 194-201.
Loftus, S. C. (2021). Basic statistics with R: reaching decisions with data. Academic Press.
Alita, D., Putra, A. D., & Darwis, D. (2021). Analysis of classic assumption test and multiple linear regression coefficient test for employee structural office recommendation. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(3), 1-5.
Araiza-Aguilar, J. A., Rojas-Valencia, M. N., & Aguilar-Vera, R. A. (2020). Forecast generation model of municipal solid waste using multiple linear regression. Global Journal of Environmental Science and Management, 6(1), 1-14.
Goh, Y. L., Goh, Y. H., Bin, R. L. L., & Chee, W. H. (2019). Predicting the performance of the players in NBA Players by divided regression analysis. Malaysian Journal of Fundamental and Applied Sciences, 15(3), 441-446.
Olsen, A. A., McLaughlin, J. E., & Harpe, S. E. (2020). Using multiple linear regression in pharmacy education scholarship. Currents in Pharmacy Teaching and Learning, 12(10), 1258-1268.
Alin, A. (2010). Multicollinearity. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 370-374.
Shrestha, N. (2020). Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39-42.
Tsagris, M., & Pandis, N. (2021). Multicollinearity. American Journal of Orthodontics and Dentofacial Orthopedics, 159(5), 695-696.
Chan, B. K., & Chan, B. K. (2018). Data analysis using R programming. Biostatistics for Human Genetic Epidemiology, 47-122.
Özkaya, U., Yiğit, E., Seyfi, L., Öztürk, Ş., & Singh, D. (2021). Comparative regression analysis for estimating resonant frequency of c-like patch antennas. Mathematical Problems in Engineering, 2021, 1-8.
