Abstract/Overview
This study analyzes recent data of accidents’ prevalence in Kenya and investigates whether there might be new trends in areas formerly not prone to accidents. Polynomials of order 6 are found best suited for accidents’ prevalence data. The graphs show that seasonal variations explain over 90% of prevalence in Central, Eastern, Nyanza, Rift-Valley and Western Provinces. The highest variation is in Nyanza with 98.54% of the prevalence rate explained by the seasonal variation. The resulting graphs display a relationship of a time series nature. Using the scatter graphs, the total numbers of accidents explain very little about accidents prevalence. The value of R2 for all the regression lines is very small. However, when the polynomials are fitted, the 6th order polynomials are found to be the most suitable. The value of the R2 varies from one province to another but is highest in North Eastern province at 87.38% and lowest in Western province at 63.72%. These figures are an indication of the significance of studying and modeling accidents’ prevalence.
Zablon, M (2024). On the Effects of Motocycle Accidents and its Trends (A Case of Kenya). Afribary. Retrieved from https://tracking.afribary.com/works/on-the-effects-of-motocycle-accidents-and-its-trends-a-case-of-kenya
Zablon, Maua "On the Effects of Motocycle Accidents and its Trends (A Case of Kenya)" Afribary. Afribary, 04 Jun. 2024, https://tracking.afribary.com/works/on-the-effects-of-motocycle-accidents-and-its-trends-a-case-of-kenya. Accessed 24 Nov. 2024.
Zablon, Maua . "On the Effects of Motocycle Accidents and its Trends (A Case of Kenya)". Afribary, Afribary, 04 Jun. 2024. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/on-the-effects-of-motocycle-accidents-and-its-trends-a-case-of-kenya >.
Zablon, Maua . "On the Effects of Motocycle Accidents and its Trends (A Case of Kenya)" Afribary (2024). Accessed November 24, 2024. https://tracking.afribary.com/works/on-the-effects-of-motocycle-accidents-and-its-trends-a-case-of-kenya