Matthew I . Ekum
I officially started going to school in 1983/1984. My first school was Eleguishi Primary School, Maroko, Victoria Island, Lagos, Nigeria. During governor Jakande regime in Lagos State, Eleguishi was merged with Massey Primary School and the school name changed to Massey Primary School. In 1987, I was transferred to Ireakari Primary School, Sari-Iganmu, Lagos, Nigeria to continue primary 4. I finished my primary education at Ireakari with Distinction in Lagos State Common Entrance and G2. I got admission to Iganmu High School, Sari Iganmu, Lagos, Nigeria where I fell in love with Mathematics. My Mathematics and Physics were A's at O'level (1995). In 1998, I got admission to study Statistics at Yaba College of Technology, Yaba, Lagos, Nigeria, in the Department of Mathematics and Statistics. During my ND programme, I did a 3-month SIWES at the then National Electric Power Authority, Marina, Lagos, Nigeria, Department of Manpower and Statistics. I graduated with Distinction at ND level in 2000. I did a compulsory one year industrial training at the then Federal Office of Statistics (FOS), Lancaster, Yaba, Lagos, Nigeria, Department of Agriculture and Household Statistics. I came back to Yaba College of Technology, Yaba to complete my HND (2004), with Upper Credit. I did my NYSC in 2004 Batch B at Government Technical College, Opposite NACAS, Kontagora, Niger State, Nigeria and passed out in August 2005. I worked for some years during which I obtained a PGD at Redeemed School of Mission, Ede, Osun State, Nigeria (2009/2010). I later went back to school to bag a degree in Statistics from the prestigious University of Ibadan, Ibadan, Oyo State, Nigeria, Department of Statistics (2010-2013). I graduated with 2nd Class Upper (Honours). I immediately went to the University of first choice, University of Lagos, Akoka, Lagos, Nigeria to obtain a masters degree in Statistics at Department of Mathematics (2014-2015). I am now a PhD student in statistics (probability distributions) at University of Lagos (2017).
Supervisors: Prof Felix Famoye and Dr. Femi Ayoola
Supervisors: Prof Felix Famoye and Dr. Femi Ayoola
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studied. Some properties of the probability distribution are discussed. The Weibull-normal distribution is found to be unimodal
or bimodal. The distribution can be right skewed or left skewed. The method of maximum likelihood estimation is suggested
to estimate the parameters of the distribution. Three numerical data sets are used to illustrate the applications of the Weibullnormal
distribution.
which outbreak started in Wuhan community of China during
December, 2019. World Health Organisation (WHO) started
reporting cases of COVID-19 on 21st January. In this research,
we aimed at forecasting new cases of COVID-19
per day, using data collected from 21st January to 10th June,
2020, spanning 142 days, by fitting polynomial models. Different
model selection criteria were used to determine the
most appropriate model among assumed models. The result
of the analysis showed that the cubic model performed
better than others. Important plots were displayed to show
the fitness of the cubic model to the data. Forecast was
made that if there is no new phase of the virus and there are
compliances to government policies to prevent the spread
of COVID-19 as advised by the Centre of Disease Control
(CDC) and WHO, then new cases of COVID-19 per day
globally would reduce significantly in coming days. The total
confirmed cases would cumulate slowly and reach 11 million
before August 2020 as the curve flattens. We recommend
that putting on face masks, washing of hands and applying
alcohol base sanitisers, cleaning surfaces with disinfectants
and keeping physical distance could help to reduce the
spread of the virus, thereby flattening the curve.
model of food production and food exports of 15 selected Economic Community of West
African States (ECOWAS) using four (4) World Development Indicators (WDI) as explanatory
variables. Data were collected from 1990 to 2013. The four WDI are Food imports (% of
merchandise imports), Agricultural land (% of land area), Fertilizer consumption (kilograms per
hectare of arable land) and Inflation (consumer prices annual %). Also, another panel regression
model was fitted for Food exports (% of merchandise exports) using Food production index as
the predictor variable. The result of the analysis shows that agricultural land and fertilizer
consumption have positive effect on the food production index of ECOWAS countries, while
food imports and rate of inflation have negative effect on food production index of the
ECOWAS countries. The analysis also shows that food exports depend on food production
among ECOWAS countries. It is seen that 98.8% of the variation in food production among
ECOWAS countries can be explained by the variations in food imports, agricultural land,
fertilizer consumption and inflation. Also, 99.6% of the variation in food export can be explained
by the variation in food production. We therefore recommend that ECOWAS countries should
increase agricultural land and fertilizer consumption and reduce food imports and rate of
inflation.
studied. Some properties of the probability distribution are discussed. The Weibull-normal distribution is found to be unimodal
or bimodal. The distribution can be right skewed or left skewed. The method of maximum likelihood estimation is suggested
to estimate the parameters of the distribution. Three numerical data sets are used to illustrate the applications of the Weibullnormal
distribution.
which outbreak started in Wuhan community of China during
December, 2019. World Health Organisation (WHO) started
reporting cases of COVID-19 on 21st January. In this research,
we aimed at forecasting new cases of COVID-19
per day, using data collected from 21st January to 10th June,
2020, spanning 142 days, by fitting polynomial models. Different
model selection criteria were used to determine the
most appropriate model among assumed models. The result
of the analysis showed that the cubic model performed
better than others. Important plots were displayed to show
the fitness of the cubic model to the data. Forecast was
made that if there is no new phase of the virus and there are
compliances to government policies to prevent the spread
of COVID-19 as advised by the Centre of Disease Control
(CDC) and WHO, then new cases of COVID-19 per day
globally would reduce significantly in coming days. The total
confirmed cases would cumulate slowly and reach 11 million
before August 2020 as the curve flattens. We recommend
that putting on face masks, washing of hands and applying
alcohol base sanitisers, cleaning surfaces with disinfectants
and keeping physical distance could help to reduce the
spread of the virus, thereby flattening the curve.
model of food production and food exports of 15 selected Economic Community of West
African States (ECOWAS) using four (4) World Development Indicators (WDI) as explanatory
variables. Data were collected from 1990 to 2013. The four WDI are Food imports (% of
merchandise imports), Agricultural land (% of land area), Fertilizer consumption (kilograms per
hectare of arable land) and Inflation (consumer prices annual %). Also, another panel regression
model was fitted for Food exports (% of merchandise exports) using Food production index as
the predictor variable. The result of the analysis shows that agricultural land and fertilizer
consumption have positive effect on the food production index of ECOWAS countries, while
food imports and rate of inflation have negative effect on food production index of the
ECOWAS countries. The analysis also shows that food exports depend on food production
among ECOWAS countries. It is seen that 98.8% of the variation in food production among
ECOWAS countries can be explained by the variations in food imports, agricultural land,
fertilizer consumption and inflation. Also, 99.6% of the variation in food export can be explained
by the variation in food production. We therefore recommend that ECOWAS countries should
increase agricultural land and fertilizer consumption and reduce food imports and rate of
inflation.