Water treatment facilities in Tanzania face challenges related to adoption rates due to varying economic conditions and technological advancements. A time-series model, specifically an ARIMA (AutoRegressive Integrated Moving Average) model, was applied to historical data from water treatment facilities across Tanzania. Robust standard errors were used to account for uncertainty in the forecasting process. The ARIMA model showed a significant positive correlation between economic indicators and adoption rates of water treatment systems, with forecasts indicating a steady increase over the next five years. The use of time-series models provides valuable insights into predicting future trends in water treatment facility adoption in Tanzania, offering engineering solutions to enhance sustainability and efficiency. Further research should explore additional factors affecting adoption rates and validate these findings through longitudinal studies. Water Treatment Facilities, Adoption Rates, Time-Series Models, ARIMA, Forecasting, Tanzania The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Magagila et al. (2000) studied this question.