Volume 9, Issue 4, July 2020, Page: 90-100
Generalized Regression Control Chart for Monitoring Crop Production
Olatunji Taofik Arowolo, Department of Mathematics & Statistics, Lagos State Polytechnic Ikorodu, Lagos, Nigeria
Matthew Iwada Ekum, Department of Mathematics & Statistics, Lagos State Polytechnic Ikorodu, Lagos, Nigeria
Received: Jan. 31, 2020;       Accepted: Apr. 7, 2020;       Published: May 28, 2020
DOI: 10.11648/j.ajtas.20200904.12      View  209      Downloads  94
Recently, Nigeria focused on Agriculture as a way to diversify her economy. Crop production, which is a proxy to measure agricultural output is considered very important. So, controlling crop production (output) among states in Nigeria is very key. In this study, the generalized regression control chart was used rather than the conventional control chart. The conventional control chart does not put into consideration factor(s) that affect crop production. The generalized regression control chart considers the factor (independent variable) that affect crop production (dependent variable). The normal distribution is a special case of the generalized regression control chart. The possibility of using Weibull regression and other non-normal models were considered. In this research, Gaussian distribution was used as the underlying distribution because it fitted the crop production data. The cost of seed/seedling was selected from a set of independent variables, because it is most significant among other independent variables. The data were collected from secondary sources, precisely National Bureau of Statistics (NBS). All the 36 states in Nigeria, including the Federal Capital Territory (FCT) were involved in the study. The result of the generalized regression control chart showed that crop production is not in control in Nigeria, which was traced to assignable cause of variation in FCT, Abuja. This implied that FCT, Abuja produced below the lower control limit of crop production, despite the relative cost of seed/seedlings.
Conventional Control Chart, Crop Production, Exponential Family, Gaussian Regression Model, Generalized Regression Control Chart
To cite this article
Olatunji Taofik Arowolo, Matthew Iwada Ekum, Generalized Regression Control Chart for Monitoring Crop Production, American Journal of Theoretical and Applied Statistics. Vol. 9, No. 4, 2020, pp. 90-100. doi: 10.11648/j.ajtas.20200904.12
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Shewhart, W. A. (1931). Statistical Method from an Engineering Viewpoint. Journal of the American Statistical Association, 26, 262-269.
Montgomery, D. C., and Woodall, W. H. (1997). A Discussion of Statistically-Based Process Monitoring and Control.
Alwan, L. C. and Roberts, H. V. (1988). Time Series Modeling for Statistical Process Control. Journal of Business and Economic Statistics, 6(1), 87–95.
Karaoglan, A. D. and Bayhan, G. M. (2014). A regression control chart for autocorrelated processes. Int. J. Industrial and Systems Engineering, 16(2), 238-256.
Wallis, W. A. and Roberts, H. V. (1956). Statistics: A New Approach. The Free Press, Chicago, III, 549-553.
Mendel, B. J. (1967). The Regression Control Chart – A Multipurpose Tool of Management, Universal Postal Union, International Bureau, Bern, Switzerland, September, 1967.
Mendel, B. J. (1967). Statistical Programs of the United States Post Office Department. Industrial Quality Control, 23(11), 535-538.
Karvanen, J. (2009). Generalized linear models. Available online at University of Helsinki, spring.
Mendel, B. J. (1969). The Regression Control Chart. Journal of Quality Technology, 1(1), 1-9.
Grant, E. L., and Leavenworth, R. S. (1980). Statistical Quality Control, 5th ed., McGraw-Hill, New York.
Juran, J. M. and Gryna, F. M. (1988). Quality control handbook, (4th. ed.), McGraw-Hill, New York.
Woodall, W. H., Spitzner, D. J., Montgomery, D. C. and Gupta, S. (2004). Using Control Charts to Monitor Process and Product Quality Profiles. Journal of Quality Technology, 36, 309–320.
NBS (2017). National Bureau of Statistics Agricultural Data. Retrieved 22 June 2017.
Arowolo, O. T., Aribike, E. and Ekum, M. I. (2017). Panel Predictive Modeling of Agricultural Production Among States in Nigeria. IOSR Journal of Mathematics (IOSR-JM), 13(5), 76-89.
Bayer, F. M., Tondolo, C. M. and Muller, F. M. (2018). Beta regression control chart for monitoring fractions and proportions. Preprint submitted to Computers & Industrial Engineering.
Canterle, D. R. and Bayer, F. M. (2017). Variable dispersion beta regressions with parametric link functions. Statistical Papers (Forthcoming), DOI: 10.1007/s00362-017-0885-9.
Browse journals by subject