Open Access Original Research Article

Assessment of French Beans Production at Kariua in Kandara, Murang’a County- Kenya

Wangui Patrick Mwangi, Argwings Otieno, Ayubu Anapapa

Asian Journal of Probability and Statistics, Page 1-16
DOI: 10.9734/ajpas/2019/v5i430141

A sample survey research was conducted in November 2017 to January 2018 at Kariua area in Murang’a county, Kenya, with a sole aim to assess the current situation experienced by the French bean farmers in the area as well as form basis for further research, in which 43 farmers were interviewed. The parameters of interest were the average input levels of various factors (manure, fertilizers and water), average spacing of the crops in the field, the average output of the beans, the general plants’ health- all these were per crop point, land sizes under French beans cultivation as well as the demographic factors like age, gender and family size. The questionnaire was the main data collecting tool. Analysis of the data collected was carried out using both descriptive and inferential statistics: Using both R software and Ms-Excel. The results showed that farmers are experiencing very low yields at peak on average and poor plant health (harvest=13.4 g, infected leaves= 8 and immature pods= 15, all per crop point). Average land size under French beans farming, D.A.P and C.A.N fertilizers applied, manures applied, crop spacing and water for irrigation were found to be approximately 79.80 m2, 4.75 decigrams, 2.49 decigrams, 24.69 grams, 9.81 cm by 27 cm and 4.38 litres respectively. Low yields and poor crop health, scarce resources, pests, infections, diseases and intercropping and were also evident in the region.

Open Access Original Research Article

Optimization of Yields and Yield Components of Sweet Potatoes (Ipomea batatas (L.) Lam) Using Organic Manure and Phosphate Fertilizer

Ireri Daniel Mwangi, Martin Miano, Lucas Macharia

Asian Journal of Probability and Statistics, Page 1-13
DOI: 10.9734/ajpas/2019/v5i430142

Sweet potato production has been faced with various constraints including small land sizes and inappropriate agronomic practices, especially on management of soil fertility. Many studies that have been carried out on the effects of application of farmyard manure on sweet potatoes yields have just been used to get the best treatment within the range of treatments used. However, the designs used in data analysis are not appropriate for optimisation process. Therefore, there is need to use an appropriate design that will optimise the yields within the limited available resources for sustainable production of sweet potatoes. The objective of this study was to determine the optimum operating settings and to optimise the yields and the yield components in sweet potatoes. The study was conducted at Chuka University horticultural demonstration farm. The experiment was laid out in a Randomised Complete Block Design and replicated three times. The treatments included cattle manure and poultry manure (0, 5, 10, 15 and 20 tons per hectare) and inorganic phosphate fertilizer (0, 20, 40, 60 and 80 tons of P2O5 per hectare). Data was collected on number of tubers, tuber diameter, length and weight of tubers per plot. Central Composite Design (CCD) was applied in the optimization process. Data obtained was analysed using R statistical software and the second order mathematical model which described the response as a function of input variables, was generated. The study found that the optimal levels of inorganic phosphate, poultry manure and cattle manure that led to maximum yield were; 2.895 tons/ ha, 7.5 tons/ ha and 14.88 tons/ha, respectively. The study demonstrated that CCD can serve as an inexpensive tool in optimization of the sweet potato yield. The study was also useful to the farmers in the area of study since they can get information on the optimal levels of application organic manure and phosphate fertilizer that would lead to maximum yields.

Open Access Original Research Article

Survival Analysis of Cholera Patients a Parametric and Non-parametric Approach

Umar M. Hassan, A. A. Abiodun

Asian Journal of Probability and Statistics, Page 1-18
DOI: 10.9734/ajpas/2019/v5i430143

Aims: The aim of this study is to investigate survival probability of cholera patients who were under follow-up and identify significant risk factors for mortality.

Methodology: In this research, we present the basic concepts, nonparametric methods (the Kaplan-Meier method and the log-rank test) and parametric method. Parametric AFT models (Exponential, Weibull, Lognormal and Log logistic) were compared using Akaike’s Information Criterion (AIC).

Results: Recorded data of 513 patients were obtained from UNICEF Cholera Hospital for Internally Displaced Persons Camps within Maiduguri, Borno State. Non-Parametric and Parametric approach were used to estimate the survival probability of the patients and examine the association between the survival times with different risk factors. The analysis shows that some factors significantly contribute to longer survival time of cholera patients. These factors include being a female, age less than twenty, being vaccinated before the infection and mild degree of dehydration.

Conclusion: The vaccination, age, sex and degree of dehydration of a cholera patient affects its survival hence, much attention should be given to older patients, degree of dehydration and vaccine (killed oral 01 with whole-cell with Bsubunit) should be administered whenever there is outbreak. When carrying out survival analysis of this kind, a Weibull model is Recommended for used while if dealing with Accelerated Failure Time models.

Open Access Original Research Article

An Application Using Stochastic Approximation Method for Improvement Specific Loss System

Rasha. A. Atwa

Asian Journal of Probability and Statistics, Page 1-7
DOI: 10.9734/ajpas/2019/v5i430144

In this paper, an application using the modified stochastic approxi-mation procedure which studied to answer the question for Robbins-Monroe procedure. The modified procedure depends on a new form. We use the case of loss system obey Negative Binomial distribution. The efficiency of the proposed procedure is calculated to determine the ways to improve the mentioned loss system, the results which are obtained show that our procedure can serve as a model of stochastic approximation with delayed observations. This new topic can be applied in many fields such as the biological, medical, life time experiments, and some industrial projects, to increase the production, where in our system we depend on, observe a lot items and this items are realized after random time delays. In this paper we referred to the conditions which improve the proposed loss system.

Open Access Original Research Article

Factors Affecting the Productivity of the Big Onion in Hambantota District during the Off Season

M. K. M. Sulochana, L. S. Nawarathna

Asian Journal of Probability and Statistics, Page 1-10
DOI: 10.9734/ajpas/2019/v5i430145

Aim: The main aim of this study is to identify the factors affecting the big onion productivity of Hambantota district during the off-season. Moreover, we identify the average productivity per acre from Hambantota district and compare it with the other areas that cultivated the big onion. Further, identify the main issues encountered in big onion cultivation in Hambantota and identify the critical contributing factors for the big onion cultivation in this area.

Place and Duration of Study: During the off seasons in 2015 to 2016 in Hambantota District.

Methodology: Sample data was collected from 201 farmers in Hambantota district. Multiple linear regression model was used to identify the factors affecting the big onion productivity in Hambantota district during the off-season. The normality assumption of the regression model was checked using Kolmogorov–Smirnov test, Shapiro Wilk normality test and Skewness and Kurtosis test. Pearson, Spearman’s Rank and Partial correlation tests were used to check the correlations between variables. Mean absolute percentage error (MAPE) and Symmetrical Mean absolute percentage error (SMAPE) values were used to validate the fitted model.

Results: By the multiple linear regression model main factors affecting the productivity of big onion in Hambantota area were Seasonal Months, Monthly Income, Subsidies Fertilizer and Cultivated Quantity. And the R-squared value was most like to 80% and this means these independent variables were described 80% of the dependent variable.  Model accuracies were reported as 98.48% and 98.49% from MAPE and SMAPE respectively. Therefore, this multiple linear regression model was suitable for this study. Further, the model determined the affected factors for the big onion cultivation in Hambantota district during the off-season.

Conclusion: Hambantota district average productivity was less than other areas. Big onion productivity of Matale is more than 2 times greater than big onion productivity of Hambantota. Off season big onion cultivation in Hambantota district is not very effective because of the average productivity is less than other areas in Sri Lanka.

Open Access Original Research Article

The Effect of Exclusive Breastfeeding on Child Survival Using Modified Kaplan Meier Model (MKMM)

Moses Longji Dashal, Kazeem Eyitayo Lasisi, Kaneng Eileen Longji

Asian Journal of Probability and Statistics, Page 1-10
DOI: 10.9734/ajpas/2019/v5i430146

Background: In Survival analysis, Kaplan-Meier estimator serves as a tool for measuring the frequency or the number of patients surviving medical treatment. Kaplan Meier estimates of survival data have become a better way of analyzing data in cohort study. Kaplan- Meier (K-M) is a non-parametric estimates of survival function that is commonly used to describe survivorship of a study population and to compare two study populations.

Aims: This research study is aimed at reducing the morbidity and mortality rate of children less than 6 months.

Methodology: 58,609 children less than six months were Exclusive Breastfed from the database. The analysis is done using both K-M and the modified K-M model to examine the effects of Exclusive Breastfeeding. The AIC and BIC was also used as the information criteria.

Results: Our results revealed that the K-M model 0.998566822 as the estimated survival probability of children under the ages of six months. Also showing, Exclusively Breastfed children stand the chance of 99% survival.

The modified K-M model also revealed 6.98276443909739 as the estimated survival probability, due to initiation of milk substitute and food supplement into the breastfeeding pattern. Showing about 70% chances of survival. Implying about 30% of the existence in one disease or the other or the risk of dying before the age of 5 years.

From the information criteria, the AIC (2.3119452169420) and BIC (7.8478797677756) in the Modified K-M are both lower compared to Existing Kaplan Meier (4.0012457354876) and (9.5371847322969) respectively. Modified K-M stand as the best model in knowing the types/amount of food to be added to breastfeeding pattern.

Conclusion: So far, the Modified Kaplan Meier Model has been verified and the findings agree that the life expectation will be improved by 99% if children are fed exclusively with breast milk while the life span is been reduced that can lead to death by 30% if the children have a mix feeding which agrees with why Exclusive Breastfeeding should be done.

Open Access Original Research Article

Detection of Non-Normality in Data Sets and Comparison between Different Normality Tests

Emmanuel O. Biu, Maureen T. Nwakuya, Nduka Wonu

Asian Journal of Probability and Statistics, Page 1-20
DOI: 10.9734/ajpas/2019/v5i430149

The paper provides five tests of data normality at different sample sizes. The tests are the Shapiro-Wilk (SW) test, Anderson-Darling (AD) test, Kolmogorov-Smirnov (KS) test, Ryan-Joiner (RJ) test, and Jarque-Bera (JB) test. These tests were used to test for normality for two secondary data sets with sample size (155) for large and (40) for small; and then test the simulated scenario with standard normal “N(0,1)” data sets; where the large samples of sizes (150, 140, 130, 130, 110 and 100) and small samples of sizes (40. 35, 30, 25, 20, 15 and 10) are considered at two levels of significance (5% and 10%). However, the aim of this paper is to detect and compare the performance of the different normality tests considered. The normality test results shows Kolmogorov-Smirnov (KS) test is a most powerful test than other tests since it detect the simulated large sample data sets do not follow a normal distribution at 5%, while for small sample sizes at 5% level of significance; the results showed the Jarque-Bera (JB) test is a most powerful test than other tests since it detects that the simulated small sample data do not follow a normal distribution at 5%. This paper recommended JB test for normality test when the sample size is small and KS test when the sample size is large at 5% level of significance.

Open Access Review Article

Multivariate Time Series Modelling with Seasonal Univariate Components; Evidence from Nigeria GDP

Taofikat Abidemi Azeez, Yusuf Olufemi Olusola, Hamzat Kayode Idris, Salawu Monsuru Micheal

Asian Journal of Probability and Statistics, Page 1-20
DOI: 10.9734/ajpas/2019/v5i430140

The patterns of GDP variables are graphically examined using time plot presented the time plot for the GDP variables concerning time presented a combined single time plot for all the considered GDP variables. The relationship, as well as the degree of relationship between/among the GDP variables, was further revealed by computing the pairwise correlation. Based on the output, each variable when crossed classified with itself have a strong positive correlation with an output of (1), while pairwise correlation reveals a positive figure with the least estimate being (0.3149), this implies that for all the variables there exist a positive correlation. All the pairwise relationship reveals a strong positive association with all the estimates revealing a value between (0.8-0.9) except ‘trade and industry' that shows a positive relationship but not strong with an estimate of (0.3149). The initial test in fitting a time series model is to examine the series for stationarity. The Augmented Dickey-Fuller test revealed that ‘Agriculture’, ‘Construction’, and Services’” satisfies the requirement of stationarity while the series ‘industry and “Trade” are non-stationary initially but later became stationary after the application of the first difference transformation which was confirmed after the application of the ADF test to the first differenced series. The Johansen co-integration's Trace test was employed to determine the order of co-integration and it was revealed that the series are cointegrated hence presentation of the equation of integration. We presented the lag length estimation criteria which revealed that the lag length of order 5 is appropriate for the VAR model as suggested by Akaike Information Criteria (AIC), Hannan-Quinn (HQ) Information Criteria, Schwarz Information Criteria (SC). The VAR(5) model was fitted for all the considered GDP variables.