Browsing by Author "Lwaho, Joseph"

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    Effect of Statistics on Collaboration for Enhancing Institutional Sustainability: A Case of Mzumbe University-Tanzania
    (Springer Nature Switzerland, 2024) Mbukwa, Justine N.; Mbegalo, Tukae; Lwaho, Joseph
    The article discusses the effects of statistics on collaboration and its potential in spearheading sustainable industrial development. In this regard, three days’ workshop were conducted by Mzumbe University Laboratory for Interdisciplinary Analysis (MULISA) in collaboration with the Ifakara health institute. These activities are statistical literacy, scientific writing and winning the research grants. The objective was to create a smooth environment for the sustainability of the statistical laboratory through knowledge. The participants in the workshop were students and lecturers at Mzumbe University, and researchers and interns from Ifakara Research Institute (IHI). The majority of the participants in all three days of workshop were males. The results revealed that the workshop has increased the research and publication activities among MULISA collaborators after the workshop compared to before the workshop. This noted benefit derived from the sharing the skills and collaboration during the workshop. Therefore, the collaborative workshops and training are the engineering tool for sustainability because it allows sharing of new knowledge. The collaboration in writing enhances thoughts, ideas, and knowledge transferability to the next generation.
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    Unfolding the potential of the ARIMA model in forecasting maize production in Tanzania
    (Business Analyst Journal (Emerald Publishing Limited ), 2023) Lwaho, Joseph; Ilembo, Bahati
    Purpose – This paper was set to develop a model for forecasting maize production in Tanzania using the autoregressive integrated moving average (ARIMA) approach. The aim is to forecast future production of maize for the next 10 years to help identify the population at risk of food insecurity and quantify the anticipatedmaize shortage. Design/methodology/approach – Annual historical data on maize production (hg/ha) from 1961 to 2021 obtained from the FAOSTAT database were used. The ARIMA method is a robust framework for forecasting time-series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung-Box test. Findings – The results suggest that ARIMA (1,1,1) is the most suitable model to forecast maize production in Tanzania. The selected model proved efficient in forecasting maize production in the coming years and is recommended for application. Originality/value – The study used partially processed secondary data to fit for Time series analysis using ARIMA (1,1,1) and hence reliable and conclusive results
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    Unfolding the potential of the ARIMA model in forecasting maize production in Tanzania
    (Emerald Publishing Limited, 2023) Lwaho, Joseph; Ilembo, Bahati
    Purpose – This paper was set to develop a model for forecasting maize production in Tanzania using the autoregressive integrated moving average (ARIMA) approach. The aim is to forecast future production of maize for the next 10 years to help identify the population at risk of food insecurity and quantify the anticipatedmaize shortage. Design/methodology/approach – Annual historical data on maize production (hg/ha) from 1961 to 2021 obtained from the FAOSTAT database were used. The ARIMA method is a robust framework for forecasting time-series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung-Box test. Findings – The results suggest that ARIMA (1,1,1) is the most suitable model to forecast maize production in Tanzania. The selected model proved efficient in forecasting maize production in the coming years and is recommended for application. Originality/value – The study used partially processed secondary data to fit for Time series analysis using ARIMA (1,1,1) and hence reliable and conclusive results.