Statistics and Operations Research Publications

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Statistics and Operations Research Publications

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    Determinants of house prices using spatial analysis: the case for Bulawayo
    (NUST, 2023) Mupondo, N.C.; Ncube, B.; Mupondo, A.; Nemahwe, S.C.
    The factors affecting house prices are crucial to Zimbabwe’s property organisation, and they necessitate an understanding of market trends and patterns in the housing industry. The primary goal of this research is to investigate the correlations between house prices and the factors that influence them to develop a model that can forecast house prices in Bulawayo. This study uses exploratory data analysis and spatial regression approaches to analyse factors affecting house prices in Bulawayo to understand how much housing costs are influenced by the availability of health services and retail stores. How does the distance to schools and the central business district (CBD) affect property prices, as well as the size of the land and the physical environment? To attain these goals, spatial analysis and local regression parameter estimates were used. The study found that many variables have both positive and negative effects on house prices across space and that the spatial lag model is the best fit for predicting house values in Bulawayo.
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    A Proportional Hazard model to establish factors that are significant in child survival.
    (IOSR, 2014) Musizvingoza, R.; Mwembe, D.; Nyamugure, P.
    This study addresses important issues affecting under-five mortality in Zimbabwe. The objective of this research is to establish factors that are significantly impacting on child survival and to determine the survival rate of children under the age of five years. Cox regression and Kaplan-Meier estimator were used for data analysis. Child survival was significantly influenced by two predictor variables, breastfeeding and immunisation status (p< 0.05). The Hazard ratios for variable breastfeeding and immunisation are 2.806 and 4.778 respectively. The survival functions for the children indicate a high survival rate especially in children who are well breastfed and those who are fully immunised. This study supports health policy interventions that enhance child survival. Immunisation and breastfeeding should be encouraged among mothers to enhance child survival.
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    Validation of the DigComp tool and evaluation of the level of digital literacy skills among Zimbabwean in-service secondary school teachers
    (POTRAZ/RCZ, 2024) Dabengwa, I.M.; Moyo, Sibonile; Gashirai, T.B.; Makaza, D.; Makoni, P.; Pasipamire, N.; Chademana, G.K.; Mufudzi, M.; Mandaza, D.; Mapfumo, S.
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    Application of the Equipment Replacement Dynamic Programming Model in Conveyor Belt Replacement: Case Study of a Gold Mining Company
    (MCSER Publishing, 2015-02) Zvipore, David C.; Nyamugure, P; Maposa, D; Lesaoana, Maseka
    As assets age, they generally deteriorate, resulting in rising operating and maintenance costs and decreasing salvage values. In this paper a comprehensive Dynamic Programming-based optimisation solution methodology is used to solve the equipment replacement optimisation problem on the replacement of conveyor belts at a Gold Mining company in Zimbabwe. Given a mining setup with one and two-year old conveyor belts the ultimate objective is to keep or replace the conveyor belt such that the overall cost of material handling is minimised within a five-year period. The findings reveal that this mining system should replace conveyor belts yearly. It is concluded that, an equipment replacement policy for conveyor belts is a necessity in a mining system so as to achieve an optimal contribution to the economic value that a mining system may accrue within a period of time.
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    Forecasting stock prices on the Zimbabwe Stock Exchange (ZSE) using Arima and Arch/Garch models.Research Academy of Social Sciences.
    (Research Academy of Social Sciences, 2014) Mutendadzamera, S.; Mutasa, Farikayi K.
    The main thrust of this study is to find out whether the stock prices on the ZSE can be predicted using ARIMA and ARCH/GARCH models. The ZSE currently does not have a model that predicts stock price movements. Thus this study attempts to explore econometrics models to predict future stock prices on the Zimbabwe Stock Exchange (ZSE) selected counters. Stock price data is differenced and tested for stationarity using KPSS test and the Augmented Dickey Fuller test. The final models are found to be Econet Wireless, ARIMA(1,1,0), Dairiboard, ARIMA(1,1,0), Delta, ARIMA(1,1,1), SeedCo, ARIMA(1,1,1) and Old Mutual, ARIMA(1,1,0). The GARCH(1,1 model for all the counters forecast better than ARIMA models considering the minimum deviations of the forecasted values from the actual ones. This is because the ARCH/GARCH models incorporate new information and analyses the series based on conditional variances where users can forecast future values with up to date information. Old Mutual had the best ARIMA model with the lowest error where as Dairiboard had the best GARCH model as shown by the minimum Schwarz criterion value of 1.365. We conclude that GARCH(1, 1) model outperforms ARIMA models in modeling stock prices in this study.