Pub Date : 2021-07-01DOI: 10.16929/ajas/2021.1165.263
A. G. Amoussou, Aristide Medenou
The export potential indicator is designed for countries that aim to support established exports by increasing exports to new or existing target markets, and several studies are being managed using various mathematical model to predict the export values. Here, we propose an econometric model that could be useful to predict the export values. We performed the ARIMA model to evaluate the realized and unrealized export potentials of products. We therefore propose to carry out actions in favor of increasing the export potential.
{"title":"Application of ARIMA models on Export potential Indicator","authors":"A. G. Amoussou, Aristide Medenou","doi":"10.16929/ajas/2021.1165.263","DOIUrl":"https://doi.org/10.16929/ajas/2021.1165.263","url":null,"abstract":"The export potential indicator is designed for countries that aim to support established exports by increasing exports to new or existing target markets, and several studies are being managed using various mathematical model to predict the export values. Here, we propose an econometric model that could be useful to predict the export values. We performed the ARIMA model to evaluate the realized and unrealized export potentials of products. We therefore propose to carry out actions in favor of increasing the export potential.","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114362325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-01DOI: 10.16929/ajas/2021.1211.266
Kabir Olalekan Omosanya, D. Agunbiade
One major factor of Economic Development of any nation is food security as the population is on increase in Nigeria. Imposition of barn and closure of border had led to increase in price, smuggling, and hoarding of rice among other problems. Diverse statistical studies had been carried out in many nation of Africa on how to improve rice production without much success. Vector Autoregressive time series modeling is proposed to analyse the relationship between population and rice production, importation, and consumption. The stationarity test reveals that only population got stationarity at first difference while others attain stationarity at zero differencing. Granger causality reported that population, rice production and importation Granger cause rate of rice consumption rate in Nigeria and in essence population, production, and importation of rice has significant effect on consumption. Important recommendation were made to enhance availability of rice for consumption
{"title":"odelling the Effect of Population on Rice Production, Consumption and Importation in Nigeria: Vector Autoregressive Model","authors":"Kabir Olalekan Omosanya, D. Agunbiade","doi":"10.16929/ajas/2021.1211.266","DOIUrl":"https://doi.org/10.16929/ajas/2021.1211.266","url":null,"abstract":"One major factor of Economic Development of any nation is food security as the population is on increase in Nigeria. Imposition of barn and closure of border had led to increase in price, smuggling, and hoarding of rice among other problems. Diverse statistical studies had been carried out in many nation of Africa on how to improve rice production without much success. Vector Autoregressive time series modeling is proposed to analyse the relationship between population and rice production, importation, and consumption. The stationarity test reveals that only population got stationarity at first difference while others attain stationarity at zero differencing. Granger causality reported that population, rice production and importation Granger cause rate of rice consumption rate in Nigeria and in essence population, production, and importation of rice has significant effect on consumption. Important recommendation were made to enhance availability of rice for consumption","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123306185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.16929/ajas/2021.1525.261
Edmond Kazungu Mudahogora, D. Ndanguza
Volatility modeling and forecasts are essential tools to all financial sectors. This paper focuses on weekly exchange rate returns of the FRW versus USD from 2012 until 2018 obtained from the National Bank of Rwanda. The aim of this paper is to formulate an appropriate GARCH model which fits the data. The GARCH(1,1) model has been selected after using required techniques of model selection.Parameters have been estimated using Least Squares method first and then validated using MCMC method. Once the chain of parameters are found, both visual inspection and basic statistics are computed and in this study, they have illustrated a good compatibility between simulation and observations. Diagnostic of convergence of the chains of parameters has been checked and ensured the model to beaccurate. The results obtained from the LSQ and MCMC methods have been compared and found to be almost similar. An agreement between the model solution and actual data is obtained and a forecast is done by concluding that the estimated values are almost similar to the real data. Hence, the identified model is accepted for forecasting and recommended for further applications.
{"title":"Modeling the Rwanda Exchange Rates by GARCH Models","authors":"Edmond Kazungu Mudahogora, D. Ndanguza","doi":"10.16929/ajas/2021.1525.261","DOIUrl":"https://doi.org/10.16929/ajas/2021.1525.261","url":null,"abstract":"Volatility modeling and forecasts are essential tools to all financial sectors. This paper focuses on weekly exchange rate returns of the FRW versus USD from 2012 until 2018 obtained from the National Bank of Rwanda. The aim of this paper is to formulate an appropriate GARCH model which fits the data. The GARCH(1,1) model has been selected after using required techniques of model selection.Parameters have been estimated using Least Squares method first and then validated using MCMC method. Once the chain of parameters are found, both visual inspection and basic statistics are computed and in this study, they have illustrated a good compatibility between simulation and observations. Diagnostic of convergence of the chains of parameters has been checked and ensured the model to beaccurate. The results obtained from the LSQ and MCMC methods have been compared and found to be almost similar. An agreement between the model solution and actual data is obtained and a forecast is done by concluding that the estimated values are almost similar to the real data. Hence, the identified model is accepted for forecasting and recommended for further applications.","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132154863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.16929/ajas/2021.1507.260
A. Diop, Abdourahmane Ndao, C. Seck, I. Faye
In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.
{"title":"Short-term prediction model for daily COVID-19 reported positive cases in Senegal","authors":"A. Diop, Abdourahmane Ndao, C. Seck, I. Faye","doi":"10.16929/ajas/2021.1507.260","DOIUrl":"https://doi.org/10.16929/ajas/2021.1507.260","url":null,"abstract":"In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123196833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.16929/ajas/2021.1545.262
Justine Dushimirimana, Stanislas Muhinyuza, J. Nzabanita
Cut rose flowers contribute to the economy and development of the export markets for several developing countries. Despite this contribution, profitable production of rose flowers is limited by wilting which leads to lower production. This paper aims to investigate the effects of Calcium foliar feed on the wilting rate of post-harvest rose flowers using the Growth Curve Model.This method was applied to the data consisting of wilting scores on five treatment groups. The Likelihood ratio test was used to test the growth curve and the equality of the growth curves in all groups.Results revealed that the expected growth curves for all groups followed different quadratic functions. The results also revealed that the wilting rate increased with the increase of calcium concentration compared to the control. This leads to a useful model for policy-makers or further analyses.
{"title":"Growth curve model for analyzing the effects of Calcium foliar feed on the wilting rate of post-harvest rose flowers","authors":"Justine Dushimirimana, Stanislas Muhinyuza, J. Nzabanita","doi":"10.16929/ajas/2021.1545.262","DOIUrl":"https://doi.org/10.16929/ajas/2021.1545.262","url":null,"abstract":"Cut rose flowers contribute to the economy and development of the export markets for several developing countries. Despite this contribution, profitable production of rose flowers is limited by wilting which leads to lower production. This paper aims to investigate the effects of Calcium foliar feed on the wilting rate of post-harvest rose flowers using the Growth Curve Model.This method was applied to the data consisting of wilting scores on five treatment groups. The Likelihood ratio test was used to test the growth curve and the equality of the growth curves in all groups.Results revealed that the expected growth curves for all groups followed different quadratic functions. The results also revealed that the wilting rate increased with the increase of calcium concentration compared to the control. This leads to a useful model for policy-makers or further analyses.","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130233778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.16929/ajas/2021.1497.259
Aba Dio, E. Deme, Idrissa Sy, A. Diop
Logistic regression model is widely used in many studies to investigate the relationship between a binary response variable Y and a set of potential predictors X. The binary response may represent, for example, the occurrence of some outcome of interest (Y=1 if the outcome occurred and Y=0 otherwise). When the dependent variable Y represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particularly, we suggest the quantile function of the GEV distribution as link function. Strokes are a serious pathology and a neurological emergency involving the vital prognosis and the functional prognosis. In Senegal, strokes account for more than 30% of hospitalizations and are responsible for nearly two thirds of mortality. In this work, we use the GVE regression model for binary data to determine the risk factors leading to stroke and to develop a predictive model of life-threatening outcomes in central Sénégal.
{"title":"A case study of Stroke patients in Senegal: application of Generalized extreme value regression model","authors":"Aba Dio, E. Deme, Idrissa Sy, A. Diop","doi":"10.16929/ajas/2021.1497.259","DOIUrl":"https://doi.org/10.16929/ajas/2021.1497.259","url":null,"abstract":"Logistic regression model is widely used in many studies to investigate the relationship between a binary response variable Y and a set of potential predictors X. The binary response may represent, for example, the occurrence of some outcome of interest (Y=1 if the outcome occurred and Y=0 otherwise). When the dependent variable Y represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particularly, we suggest the quantile function of the GEV distribution as link function. Strokes are a serious pathology and a neurological emergency involving the vital prognosis and the functional prognosis. In Senegal, strokes account for more than 30% of hospitalizations and are responsible for nearly two thirds of mortality. In this work, we use the GVE regression model for binary data to determine the risk factors leading to stroke and to develop a predictive model of life-threatening outcomes in central Sénégal.","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121886485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.16929/ajas/2021.1473.258
M. Faye, A. Deme, A. Diongue
In this paper, we have used the Generalized Additive Model (GAM) to investigate the relationships between high temperature and daily number of deaths in Niakhar, a Sehalian-Sudanese climate in central Senegal. Daily data on number of deaths and meteorological variables over the period of 1983-2013 were considered. Descriptive statistics show that, over the study period, the total of non-accidental deaths were 12,798, among which we notice that 490 persons (3.83%) died of cardiovascular disease, 1,015 persons (7.93%) died of respiratory disease, 3,970 persons (31.02%) died of certain infectious and parasitic diseases, and 224 persons (1.75%) died of nervous system disease From the GAM model, we observe that high temperature significantly increased the relative risk (RR)Indeed, relative risk of deaths due to cardiovascular disease is 1.034 with a 95% confidence intervals (CI) 1.025 to 1.044, while it is 1.030 with a 95% CI 1.026 to 1.033 for certain infectious and parasitic disease. For respiratory disease, the RR is 1.012 with a 95% CI 1.007 to 1.017, and for nervous system disease, the relative risk is 1.034 with 95% CI 1.026 to 1.043.
{"title":"Modeling the relationship between temperature and mortality : a case study in SENEGAL","authors":"M. Faye, A. Deme, A. Diongue","doi":"10.16929/ajas/2021.1473.258","DOIUrl":"https://doi.org/10.16929/ajas/2021.1473.258","url":null,"abstract":"In this paper, we have used the Generalized Additive Model (GAM) to investigate the relationships between high temperature and daily number of deaths in Niakhar, a Sehalian-Sudanese climate in central Senegal. Daily data on number of deaths and meteorological variables over the period of 1983-2013 were considered. Descriptive statistics show that, over the study period, the total of non-accidental deaths were 12,798, among which we notice that 490 persons (3.83%) died of cardiovascular disease, 1,015 persons (7.93%) died of respiratory disease, 3,970 persons (31.02%) died of certain infectious and parasitic diseases, and 224 persons (1.75%) died of nervous system disease From the GAM model, we observe that high temperature significantly increased the relative risk (RR)Indeed, relative risk of deaths due to cardiovascular disease is 1.034 with a 95% confidence intervals (CI) 1.025 to 1.044, while it is 1.030 with a 95% CI 1.026 to 1.033 for certain infectious and parasitic disease. For respiratory disease, the RR is 1.012 with a 95% CI 1.007 to 1.017, and for nervous system disease, the relative risk is 1.034 with 95% CI 1.026 to 1.043.","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132816125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.16929/ajas/2020.829.244
Tagbo Innocent Aroh, Ousman Saine, Soumaila Demb'el'e, G. Lo
In this paper we want to find a statistical rule that assigns a passing or failing grade to students who undertook at least three exams out of five in a national exam, instead of completely dismissing these students. While it is cruel to declare them as failing, especially if the reason for their absence it not intentional, they should have demonstrated enough merit in the three exams taken to deserve a chance to be declared passing. We use a special classification method and nearest neighbors methods based on the average grade and on the most modal grade to build a statistical rule in a supervised learning process. The study is built on the national GABECE educational data which is a considerable data covering seven years and all the six regions of the Gambia.
{"title":"A Supervised Hybrid Statistical Catch-Up System Built on GABECE Gambian Data","authors":"Tagbo Innocent Aroh, Ousman Saine, Soumaila Demb'el'e, G. Lo","doi":"10.16929/ajas/2020.829.244","DOIUrl":"https://doi.org/10.16929/ajas/2020.829.244","url":null,"abstract":"In this paper we want to find a statistical rule that assigns a passing or failing grade to students who undertook at least three exams out of five in a national exam, \u0000 instead of completely dismissing these students. While it is cruel to declare them as failing, especially if the reason for their absence it not intentional, they should have \u0000 demonstrated enough merit in the three exams taken to deserve a chance to be declared passing. We use a special classification method and nearest neighbors methods based on the \u0000 average grade and on the most modal grade to build a statistical rule in a supervised learning process. The study is built on the national GABECE educational data which is a \u0000 considerable data covering seven years and all the six regions of the Gambia.","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127414814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.16929/ajas/2020.929.248
C. Gbèmavo
The aim of this study was to predict the Jatropha~curcas plant yield through an Artificial Neural Network (ANN) Multi-Layer Perceptron (MLP) model. The predictive ability of the developed model was tested against the Multiple Linear Regression (MLR) using performance indexes. According to the performance indexes the use of ANN-MLP model improved J.~curcas plant yield prediction comparatively to MLR model
{"title":"Mathematical prediction of the Jatropha curcas L. plant yield: comparing Multiple Linear Regression and Artificial Neural Network Multilayer Perceptron models","authors":"C. Gbèmavo","doi":"10.16929/ajas/2020.929.248","DOIUrl":"https://doi.org/10.16929/ajas/2020.929.248","url":null,"abstract":"The aim of this study was to predict the Jatropha~curcas plant yield through an Artificial Neural Network (ANN) Multi-Layer Perceptron (MLP) model. The predictive ability of the developed model was tested against the Multiple Linear Regression (MLR) using performance indexes. According to the performance indexes the use of ANN-MLP model improved J.~curcas plant yield prediction comparatively to MLR model","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127162910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.16929/ajas/2020.451.245
Ousman Saine, Soumaila Demb'el'e, G. Lo, M. C. Haidara
As the first paper of a series of exploratory analysis and statistical investigation works on the Gambian GABECE data based on a variety of statistical tools, we wish to begin with a thorough unsupervised learning process through descriptive and exploratory methods. This will lead to a variety of discoveries and hypotheses that will direct future research works related to this data
{"title":"Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process","authors":"Ousman Saine, Soumaila Demb'el'e, G. Lo, M. C. Haidara","doi":"10.16929/ajas/2020.451.245","DOIUrl":"https://doi.org/10.16929/ajas/2020.451.245","url":null,"abstract":"As the first paper of a series of exploratory analysis and statistical investigation works on the Gambian GABECE data based on a variety of statistical tools, \u0000 we wish to begin with a thorough unsupervised learning process through descriptive and exploratory methods. This will lead to a variety of discoveries and hypotheses that \u0000 will direct future research works related to this data","PeriodicalId":332314,"journal":{"name":"African Journal of Applied Statistics","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122895923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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