Research
Monitoring Coronavirus Disease 2019 (COVID-19) Pandemic Outbreak in Africa
This study is a monitoring analysis of COVID-19 in Africa. The data used for the study is sourced from the Africa Centre for Disease Control (Africa CDC) as t 10:00 PM on the 24th of April, 2020, which comprises a number of Africa countries with laboratory-confirmed cases, number of death, and number of discharged/recovered cases. The quality pandemic monitoring/control tools used in this study is the fish-bone diagram, Pareto analysis, control chart, bar chart, and pie chart. The fish-bone diagram depicts the likely symptoms to check out for in a patent infected by COVID-19; the Pareto analysis shows that 14 countries to the left of this line (South Africa, Egypt, Morocco, Algeria, Cameroon, Ghana, Ivory Coast, Djibouti, Tunisia, Nigeria, Guinea, Niger, and Burkina Faso) constitute 80% of all the infected countries; the trend analysis shows that the spread of the pandemic is still on an increasing rate; and lastly, from the performance assessment, it is seen that the spread is still under control from the pie chart while the death rate is already out of control.
Performance Assessment of Mean Methods in Estimating Process Capability for Non-Normal Process for Weibull Family Life Distribution
This paper compares the performances of Gini Mean, Clements and Box- Cox transformation methods for estimating process capability Indices when the distribution of the process data is (skewed) non-normal. The use of Process Performance Index (PPI) is implored for process capability analysis (PCA) using Weibull distribution. Simulation of data was also carried out using R software using a decision interval (target point) of 1.0 and 1.5. Performance assessment was carried out using Boxplots, descriptive statistics and the root mean square deviation. The following were the findings from the results. The Gini mean difference based process capability indices performs best in estimating the process capability indices closest to a set target for varying distribution parameters at different sample sizes, followed by Clements and lastly, the Box-Cox transformation method [10, 19].
