Statistical Trend Analysis of Residential Water Demand in Kisumu City, Kenya
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 3, May 2015, Pages: 112-117
Received: Mar. 30, 2015; Accepted: Apr. 9, 2015; Published: Apr. 18, 2015
Views 3941      Downloads 172
Authors
Robert Nyamao Nyabwanga, Department of Mathematics, Kisii University, Kisii, Kenya
Edgar Ouko Otumba, Department of Statistics and Actuarial Science, Maseno University, Maseno, Kenya
Fredrick Onyango, Department of Statistics and Actuarial Science, Maseno University, Maseno, Kenya
Simeyo Otieno, School of Business and Economics, Jaramogi Oginga Odinga University, Bondo, Kenya
Article Tools
Follow on us
Abstract
This study sought to analyse trend in the monthly water demand data series in Kisumu city at both seasonal and non-seasonal levels using the parametric method of Ordinary Least Squares (OLS) and non-parametric methods of Mann-Kendall tau and Sen's T test. Sen’s test was applied to validate the Mann Kendall trend test and to estimate the magnitude of the trend and its direction. The significance of the slope of the OLS equation was tested using the F-Test based on the Analysis of Variance (ANOVA). Secondary monthly water consumption data obtained from KIWASCO for the period January 2004 to December 2013 were used. Using logarithmically transformed data, the study established by OLS that residential water demand in Kisumu City had a significant increasing trend (FCalc=(105.13) > F(1;119)(α=0:05)=(5.15)). Kendall's tau test corroborated the OLS results of a significant increasing trend. The Sens T test indicated that most of the months registered significant upward trend with Sen’s slope estimates showing positive rates of change in residential water demand for these months.
Keywords
Trend, Kendall’s Tau, OLS, Sen’s T, Residential Water Demand
To cite this article
Robert Nyamao Nyabwanga, Edgar Ouko Otumba, Fredrick Onyango, Simeyo Otieno, Statistical Trend Analysis of Residential Water Demand in Kisumu City, Kenya, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 3, 2015, pp. 112-117. doi: 10.11648/j.ajtas.20150403.16
References
[1]
Gupta, S., P.(2005). 'Statistical Methods, Sultan Chand and Sons Educational Publishers, New Delhi.
[2]
Kahya, E. and S. Kalayci, 2004. Trend analysis of stream flow in Turkey. J. Hydrol., 289: 128-144. DOI:10.1016/j.jhydrol.2003.11.006.
[3]
KIWASCO (2007). Kisumu Water and Sewerage Company Strategic Plan 2007-2012.
[4]
Machiwal D, Jha MK.(2008). Comperative evaluation of statistical tests for time series analysis: Application to hydrological timeseries. Hydrological Sciences, Journal-des Sciences Hydrologiques] 53(3): 353–366
[5]
Maftei C., Barbulescu, A. (2008). Statistical Analysis of Climate Evolution in Dobrudja Region. Proceedings of the World Congress on Engineering 2008 Vol II WCE 2008, July 2-4, London, U.K.
[6]
Onoz, B. and Bayazit, M. (2003). The power of statistical tests for trend detection.
[7]
Sen PK (1968). Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 39:1379–1389
[8]
Wagah, G., Onyango, G. and Kibwage, J. (2010). Accessibility of water services in Kisumu Municipality Kenya. Journal of Geography and Regional Planning. Vol. 3(4), pp 114-125.
[9]
World Bank (2003) Water Resources Strategy. World Bank, Washington, D.C.
[10]
World Bank (2005) Water for the Urban Poor: Water Markets, Household Demand, and Service Preferences in Kenya. Water Supply and Sanitation Sector Board p.
[11]
Von Storch, H., (1995). Misuses of statistical analysis in climate research, in Analysis of Climate Variability: Applications of Statistical Techniques, edited by H. V. Storch and A. Navarra, pp. 11-26, Springer-Verlag, NewYork.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186