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@article{Sagamiko_Mbare_2021, title={Modelling Road Traffic Accidents Counts in Tanzania: A Poisson Regression Approach}, volume={47}, url={http://tjs.udsm.ac.tz/index.php/tjs/article/view/48}, DOI={10.4314/tjs.v47i1.26}, abstractNote={<p>Road traffic accidents have become serious threats to Tanzanians in recent years. The outcry<br>emanates from the increasing prevalence of negative effects of accidents on human lives,<br>properties, environments and the economy. Poisson regression model was used to study the<br>relationship between road accidents and the factors facilitating them in Tanzania. Count data on<br>yearly road traffic accidents for Tanzania covering the period 1993 to 2019 were used. Due to<br>over-dispersion of Poisson regression model, quasi-Poisson regression model was found the most<br>appropriate approach for the analysis of these data. Results indicated that all predictors are<br>significant under Poisson regression model with p-value less than 0.05 but high speed was found<br>insignificant using quasi-Poisson regression model. All factors causing road accidents predicted<br>minor increase of accidents, showing that current control measures on road accidents are likely to<br>be effective.</p> <p><br><strong>Keywords</strong>: Road accidents; Poisson regression; Over-dispersion; Deviance; Variance inflation<br>factor.</p>}, number={1}, journal={Tanzania Journal of Science}, author={Sagamiko, Thadei and Mbare, Nyimvua}, year={2021}, month={Feb.}, pages={308–314} }