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Olayemi Michael Sunday, Olubiyi Adenike Oluwafunmilola
American Journal of Theoretical and Applied Statistics, Volume 10; https://doi.org/10.11648/j.ajtas.20211001.12

Njoroge Elizabeth Wambui, Koske Joseph, Mutiso John
American Journal of Theoretical and Applied Statistics, Volume 10; https://doi.org/10.11648/j.ajtas.20211001.15

Gyeongseung Han, Jeewon Han, Seungmin Han, Hyunkyung Jeong
American Journal of Theoretical and Applied Statistics, Volume 10; https://doi.org/10.11648/j.ajtas.20211001.11

Hussein Salifu, Nyemekye Gabriel, Isaac Zingure
American Journal of Theoretical and Applied Statistics, Volume 10; https://doi.org/10.11648/j.ajtas.20211002.12

Bezarede Mekonnen, Tadesse Ayele
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200902.12

Abstract:
Education is a process through which mankind transmit experience, new findings and value accumulate over time with the aim of individuals and societies, to make all around participation in the development process. Sufficient healthy intake of food is essential to brain function. Moreover, maximizing brain function is a prime factor in seizing appropriate cognitive capability for example, ability to focus, comprehension, evaluation, and application in learning. Therefore, food is advisable take account for quality and stuffiness of food to protect our students from different infection and declining on their academic performance. The study was trying to assess the impact of Debre Berhan university cafeteria food related problems on the academic achievement of students. The general objective of the study is to analyze statistically the impact of food related problems on academic achievement of students. The study used well organized questionnaire and the source of data is primary data which will done by direct contact of respondents and secondary data which will be obtained from the registrar of Debre Berhan university. The collected data is analyzed by using descriptive and inferential statistics. The sample size determination is made by using statistical formula. Descriptive statistics and multiple linear regression analysis is applied to examine the relationship between the dependent variable academic achievement and food related problem independent variables. Multiple linear regression model is a mathematical equation that provides prediction of the value of dependent variable based on known value of independent variables. The analysis of multiple linear regression revealed that the academic achievement of students was mostly affected by adequacy of food supply, quality of cafeteria sanitation, health related problem, food sanitation and personal hygiene of cafeteria workers, time of cafeteria and those factors affect the academic achievement of students.
Girma Woldemichael, Abebech Endashaw, Abinet Tadesse, Berhanu Achamo
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200903.13

Abstract:
Soil is one of the natural resource and under high pressure that is increasing from year to year, resulting in poor fertility. The objective of this study was to assess the attitudes of farmer’s perception to soil fertility management practices. In order to achieve these objectives, random sampling methods was used to select respondents in the study area. The data was collected by using field observation, questionnaires and key informant discussion. The collected data were analyzed through descriptive statistics. The survey revealed that the factors that hinder farmers from using improved ways of soil fertility management practices are: labor problem 27.5%, economic problem 20%, lack of awareness and demographic factors 37.5%. In the Kalisha District, there are a number of major indigenous soil fertility management practices (SFMP) that are using by almost all farmers such as using cattle dung, straw, intercropping legumes crops in their farm land and use of enset in homegarden area. In other form, this study showed that, in Kalisha District the attitudes of farmers to soil fertility management is less, due to the awareness gap in society and less interventions of development agents. Therefore the farmers should be aware of soil fertility management practices on both biological and physical measures to restore soil fertility and they have to scale up the indigenous SFMP to maintain the productivity of the soil.
Janiffer Mwende Nthiwa, Ali Salim Islam, Pius Nderitu Kihara
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.20

Abstract:
Nonparametric methods are rich classes of statistical tools that have gained acceptance in most areas of statistics. They have been used in the past by researchers to fit missing values in the presence of auxiliary variables in a sampling survey. Nonparametric methods have been preferred to parametric methods because they make it possible to analyze data, estimate trends and conduct inference without having to fully specify a parametric model for the data. This study, therefore, presents some new attempts in the complex survey through the nonparametric imputation of missing values by the use of both penalized splines and neural networks. More precisely, the study adopted a neural network and penalized splines to estimate the functional relationship between the survey variable and the auxiliary variables. This complex survey data was sampled through a cluster - strata design where a population is divided into clusters which are in turn subdivided into strata. Once missing values have been imputed, this study performs a model calibration with auxiliary information assumed completely available at the cluster level. The reasoning behind model calibration is that if the calibration constraints are satisfied by the auxiliary variable, then it is expected that the fitted values of the variable of interest should satisfy such constraints too. The population total estimators are derived by treating the calibration problems at cluster level as optimization problems and solving it by the method of penalty functions. A Monte Carlo simulation was run to assess the finite sample performance of the estimators under complex survey data. The efficiency of the estimator’s performance was then checked by MSE criterion. A comparison of the penalized spline model calibration and neural network model calibration estimators was done with Horvitz Thompson estimator. From the results, the two nonparametric estimator’s performances seem closer to that of Horvitz Thompson estimator and are both unbiased and consistent.
Abdushukurov Abdurahim Ahmedovich, Kakadjanova Leyla Reshitovna
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.15

Abstract:
In the analysis of statistical data in biomedical treatments, engineering, insurance, demography, and also in other areas of practical researches, the random variables of interest take their possible values depending on the implementation of certain events. So in tests of physical systems (or individuals) on duration of uptime values of operating systems depend on subsystems failures, in insurance business insurance company payments to its customers depend on insurance claims. In such experimental situations, naturally become problems of studying the dependence of random variables on the corresponding events. The main task of statistics of such incomplete observations is estimating the distribution function or what is the same, the survival function of the tested objects. To date, there are numerous estimates of these characteristics or their functionals in various models of incomplete observations. In this paper investigated the asymptotic properties of sequential processes of independence of the integral structure and uniform versions of the strong law of large numbers and the central limit theorem for integral processes of independence by indexed classes are established. The obtained results can be used to construct statistics of criteria for testing a hypothesis of independence of random variables on the corresponding events.
Langat Reuben Cheruiyot
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.13

Abstract:
In survey sampling statisticians often make estimation of population parameters. This can be done using a number of the available approaches which include design-based, model-based, model-assisted or randomization-assisted model based approach. In this paper regression estimation under model based approach has been studied. In regression estimation, researchers can opt to use parametric or nonparametric estimation technique. Because of the challenges that one can encounter as a result of model misspecification in the parametric type of regression, the nonparametric regression has become popular especially in the recent past. This paper explores this type of regression estimation. Kernel estimation usually forms an integral part in this type of regression. There are a number of functions available for such a use. The goal of this study is to compare the performance of the different nonparametric regression estimators (the finite population total estimator due Dorfman (1992), the proposed finite population total estimator that incorporates reflection technique in modifying the kernel smoother), the ratio estimator and the design-based Horvitz-Thompson estimator. To achieve this, data was simulated using a number of commonly used models. From this data the assessment of the estimators mentioned above has been done using the conditional biases. Confidence intervals have also been constructed with a view to determining the better estimator of those studied. The findings indicate that proposed estimator of finite population total that is nonparametric and uses data reflection technique is better in the context of the analysis done.
Kubugha Wilcox Bunonyo, Emeka Amos
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200903.17

Abstract:
This research was carried out to investigate the impact of treatment parameter on blood flow in an atherosclerotic artery by formulating a momentum equation governing the flow in dimensional form which was scaled to dimensionless form using some important scaling parameters. The equation was solved analytical and obtained velocity profile, thereafter the volumetric flow rate, shear stress were calculated analytically and some pertinent physical parameters were obtained, finally Mathematica codes were developed to simulation the analytical results by varying the pertinent parameters to investigate the influence of the physical parameters on the blood flow profile, volumetric flow rate and the shear stress. In conclusion, it is seen that some of the pertinent parameters RT, Re, Da, M, ω, δ caused the flow to improve while the others did not, taken t=5 seconds. This research is very helpful in providing an insight of the treatment excessive intake of fatty substance.
Nicholas Pindar Dibal, Christopher Akas Abraham
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200903.14

Abstract:
Many real life events involves several interacting variables, hence multivariate statistical tool is necessary for appropriate analysis and interpretation. Discriminant analysis (DA) is one of the commonly used multivariate method in various fields of study including education, finance, environment, medicine etc., where complex data analysis and interpretation is required. This paper demonstrates and illustrate approaches in presenting how the discriminant analysis can be carried out on 335 (40 diabetics and 295 non-diabetic) patients and how the output can be interpreted using the Fisher’s linear Discriminant function (FLDF). The performance of FLDF was adjudged based on the percentage of correct reclassification of the original observation to yield the discriminant scores from the functions. Up to 65.4% correct classification was achieved, and similarly 62.7% percent of the cross-validated grouped cases were correctly classified into either being a Diabetic or non-diabetic patient. Patient’s age and gender were found to be the two most important contributing variables in classifying a patient between the two groups.
Yahaya Haruna Umar, Matthew Adeoye
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.11

Abstract:
Understanding and forecasting the behavior of volatility in stock market has received significant attention among researchers and analysts in the last few decades due to its crucial roles in financial markets. Portfolios managers, option traders, and market makers are all interested in the possibility of forecasting, with a reasonable level of accuracy. This study examined the volatility on the Nigeria stock market by comparing two Markov regime switching Autoregressive (MS-AR) Models estimated at different lagged values using the Nigeria stock exchange monthly All Share Index data from 1988 to 2018 in the Central Bank of Nigeria (CBN) Statistical Bulletin. It was found that factors like financial crisis, information flow, trading volume, economical aspects and investor’s behavior are the causes of volatility in the stock market. The results and forecasts obtained from the statistical analysis in this research showed that the stock market will experience a steady growth in 2020 and beyond. Also, the stock market is experiencing fluctuations in the price indices which show that over the years, investors have been exposed to some certain risks in the time past. We therefore recommended that researchers should focus more attention in developing robust statistical model that will reflect and continue to monitor future trends and realities.
Henrietta Ebele Oranye, Kingsley Chinedu Arum, Agnes Nneoma Kalu
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.17

Abstract:
New born baby is a gift from God and what a newborn baby looks like is not a baby model, rather a newborn baby looks varies from baby to baby in terms of weight, height and head circumferences. In this research, a sample of 200 male and female babies was used for the analysis. The aim is to identify if there is a significant difference between the means of the variables considered. In this research, three variables were considered for both male and female babies at birth. The result showed that the mean birth weight is 3.55kg and 3.39kg for male and female babies respectively. The mean height and head circumference of female babies recorded higher than their male counterpart. The Hoteling’s T2-test showed that there is a significant difference between the mean vectors of the variables considered; hence a discriminant analysis was conducted. The discriminant function obtained fairly classifies the group at 42% error rate. From the results gotten, there is significant difference between the height, weight and head circumference of male and female babies and conclude that male babies are heavier in terms of weight while female babies have bigger head circumference than the male babies.
Jama Mohamed
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.18

Abstract:
In recent years, the Consumer Price Index (CPI) prediction has attracted the attention of many researchers due to its excellent measurement of macroeconomic performance. It is an important index that is used to measure the rate of inflation or deflation of commodities. In this paper, Autoregressive Integrated Moving Average (ARIMA) and regression with ARIMA errors, where the covariate is the time, were compared to forecast Somaliland Consumer Price Index using monthly time series data from 2013 – 2020. The study used and applied both models to produce the necessary forecasts. Also, Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and other model accuracy measures were used to measure model’s predictive ability. By utilizing these methods, it is obtained that ARIMA (0, 1, 3) is the most suitable model for predicting CPI in Somaliland. Furthermore, the diagnostic tests show that the model presented is reliable and appropriate for forecasting Somaliland CPI data. The study results obviously indicate that CPI in Somaliland is more likely to proceed on an upward trend in the coming year. The study guides policymakers to use strict monetary and fiscal policy measures to address Somaliland’s inflation.
Abraham Okolo, Saidu Sauta Abdulkadir, Ikeme John Dike, Abubakar Adamu
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200903.16

Abstract:
This research examine different covariance structures for experimental design with repeated measure data. Multiple responses taken sequentially from same experimental unit at different periods of time for quantitative data are referred as Repeated Measurement. Weight of 105 broilers in grams for six group obtained from jewel farm Gombe were used as research materials/data. Eleven different covariance structures including the modified one (UN, UNC, TOEP, TOEPH, ANTE(1), AR(1), ARH(1), CS, CSH, HF and ARFA(1)) were examined. AIC, AICC, BIC, HQIC, CAIC and the modified criteria ASIC were used to examine covariance structures and bring the best among them using the named information criteria. The result shows that sphericity assumptions was violated a such the best covariance structure was ARH(1) while the least structure was CSH. Also on the basis of goodness of fit criteria HQIC was found to be the best information criteria. When examined the best information criteria and covariance structure with the modified ones, the modified ASIC and ARFA(1) found to be the best. In conclusion examine different covariance structures with repeated measure data give a very good result defending on the kind of data.
Habtamu Dessie, Yenefenta Wube, Belete Adelo, Eskeziaw Abebe
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200903.11

Abstract:
Congestive heart failure is a complex clinical syndrome of functional or structural impairment in the heart. Nowadays heart failure is common and increasing in the world and researches on this area is limited. Therefore the aim of the present study was to analyze and quantify the impact of modelling heart failure survival allowing for covariates with time varying effects known to be independent predictors of overall mortality in this clinical setting. A retrospective cohort study was conducted on CHF patients who were on treatment follow up at both WGH and DRH from January 1, 2010 to December 30, 2016. A total of 487 patients were selected by using simple random sampling from the patient's medical record. Semi parametric, parametric PH models and AFT models was employed to identify the best model which shown as the real causation of factors with the outcome of CHF which is death. The Weibull accelerated failure time model result showed that the risk factors related to accelerating or decelerating the lifespan were age (TR=0.962, p=0.000), Residence (rural) (TR=1.24, p=0.019), Nutritional (Poor) (TR=0.582, p=0.000), Smoking (TR=0.774, p=0.005), Alcoholism (TR=1.394, p=0.010), Diabetes mellitus (TR=0.49, p=0.000), Hypertension (TR=0.079, p=0.019), Stroke (TR=0.799, p=0.014), Coronary Artery disease (TR=0.276, p=0.012), Tuberculosis bacillus (TR=0.103, p=0.000) as a co morbidity and the interaction between age and Tuberculosis bacillus (p=0.000), age and Coronary artery disease (p=0.041), Diabetes mellitus with Hypertension (p=0.000), Hypertension with Nutritional status (p=0.000) and age with time (p=0.000) were found statistically significant. The Weibull accelerated failure time model performed better explain the effect of predictors than other Cox and parametric PH models. Thus, researchers should use parametric AFT models to see regression varying effect covariates. Frequent monitoring and follow up of Patients with heart failure should be adopted.
Tofik Mussa Reshid
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200903.12

Abstract:
Ethiopia was one of the countries least developed and it is among the countries in the bottom in the rank of GDP’s that UN lists. However, nowadays Ethiopia is one of the fastest growing economies in the world. In Ethiopia much effort has been made to build the national economy. Ethiopia has made significant strides towards becoming a middle income country by 2025. This paper provides an overview of Box-Jenkins model for temporal data. In this research we used time series analysis of some of Ethiopian economic features such as GDP, GDP growth rate and inflation rate. Box-Jenkins model was used to analyze 35-year data (1981-2015). GDP, GDP growth rate, and inflation rate were variables under the study to describe persistence change and to forecast future behaviors. We tried to find best model for description and predictive model for these series using different model selection tools. We compared different orders of Autoregressive Integrated Moving Average (ARIMA) using AIC, BIC and MSE to fit the observed data. The best from compared was ARIMA (2, 2, 2) for GDP, ARIMA (2, 1, 2) for GDP growth rate and ARIMA (1, 1, 1) for inflation rate. Since forecasting is important for many purposes, we forecast the series from best ARIMA models. Five year forecast showing that GDP is an increasing trend and the average forecast of GDP rates is showing an average of 10.028.
Peter Tumwa Situma, Leo Odongo
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200902.11

Abstract:
This paper considers the problem of estimating the parameters of Poisson-Exponential (PE) distribution under progressive type-I interval censoring scheme. PE is a two-parameter lifetime distribution having an increasing hazard function. It has been applied in complementary risks problems in latent risks, that is in scenarios where maximum lifetime values are observed but information concerning factors accounting for component failure is unavailable. Under progressive type-I interval censoring, observations are known within two consecutively pre-arranged times and items would be withdrawn at pre-scheduled time points. This scheme is most suitable in those cases where continuous examination is impossible. Maximum likelihood estimates of Poisson-Exponential parameters are obtained via Expectation-Maximization (EM) algorithm. The EM algorithm is preferred as it has been confirmed to be a more superior tool when dealing with incomplete data sets having missing values, or models having truncated distributions. Asymptotic properties of the estimates are studied through simulation and compared based on bias and the mean squared error under different censoring schemes and parameter values. It is concluded that for an increasing sample size, the estimated values of the parameters tend to the true value. Among the four censoring schemes considered, the third scheme provides the most precise and accurate results followed by fourth scheme, first scheme and finally the second scheme.
Hilda Chepkosgei Rotich, Joel Cheruiyot Chelule, Herbert Imboga
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200906.15

Chinonso Micheal Eze, Oluchukwu Chukwuemeka Asogwa, Charity Uchenna Onwuamaeze, Nnaemeka Martin Eze, Chukwunenye Ifeanyi Okonkwo
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200901.11

Abstract:
This work provides a general overview and consideration of Box-Jenkins models for temporal data and its extension known as Fourier residual autoregressive moving average models. We examined the modeling and forecasting of malaria incidence rate during pregnancy at Bishop Shannahan Hospital, Nsukka using Autoregressive Integrated Moving Average (ARIMA) Models propounded by Box and Jenkins. We adoptted the Box-Jenkins methodology to build ARIMA model for malaria incidences during pregnancy for a period of 10 years spanning from January 2006 to December 2016. Among the candidate models considered, ARIMA (3,1,1) was identified to be the most robust based on some model performance measures. The model was further improved upon by incorporating Fourier residual modification on the fitted ARIMA model. The Fourier Residual Autoregressive Moving Average (FARIMA) model obtained yielded improved result. Besides, model evaluation criterion such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Mean Bias Error (MBE), Mean Absolute Scaled Error (MASE), were used to access the models. FARIMA Model out performed ARIMA Model. Several time series plots and tests like augmented dickey fuller test, correlogram, Ljung-Box test for serial correlation of the residuals, etc were carried out in this study to test for stationarity, identify the order of ARIMA model and serial correlation residual respectively.
Bezarede Mekonnen, Legesse Alamrie
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200901.12

Abstract:
Drugs are damaging by their nature. These substances produce changes in behavior function by altering the chemistry of the brain. Once brain function is altered, a person experiences physical, psychological, and behavioral changes as a direct result. Changes in physical and psychological functioning cause damage to the mind, body, behavior and can harm the social relationships. According United Nations Office for Drug Control and Crime Prevention report, the use of substances such as alcohol, khat, and tobacco has become one of the rising major public health and socioeconomic problem worldwide. The main objective of this study is to identify the risk factors for drug addiction of youth in debre berhan town. In general, this study is expected to be useful for the parents, the youth themselves, government, nongovernmental organization, and city administration in providing primary information and assist the concerned bodies to come up with appropriate intervention strategies that can help to curb the drug problem. The data is primary and cross-sectional. Descriptive statistics and the binary logistic regression analysis is applied to examine the association between the binary outcome dependent variable and explanatory variables. The binary logistic model revealed that the numbers of youth those are addicted in the age group 20-25 are much greater than from the youth those are addicted in the age group 15-19 and 25-30, youths with family members who use drugs are more likely to be drug addicts, and also those who are orthodox religion followers are more likely to be addicted than Muslim, protestant and other religion follower, additionally youth who are single are more drug user than married, divorced and widowed.
Richard Puurbalanta, Mark Adjei, Vida Afosaa
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200903.15

Abstract:
The importance of improved healthcare services under Ghana’s National Health Insurance Scheme (NHIS) is nationwide admitted. However, service improvement for insurance schemes comes with extra cost. To fill the funding gap, insurance schemes typically charge enhanced premiums. This requires clients’ approval and cooperation to implement. For this reason, this study was conducted to assess Ghana’s NHIS subscribers’ willingness to pay (WTP) enhanced premiums for improved services. Some socio-economic and demographic factors were used as covariates. WTP, being the dependent variable, was categorized into high WTP, moderate WTP, low WTP, and no WTP enhanced premiums. The likelihood of a client falling in a particular WTP category was examined using the Cumulative Ordinal Probit (COP) regression model. A likelihood ratio chi-square of 58.82 with p < 0.000 shows that the model was statistically significant, and fit for prediction. Results showed that age-groups 18-30, 30–45, unemployed, tertiary education, and level of income significantly influenced WTP. Predictions showed that for any average national health insurance user, the probability of being in high WTP, moderate WTP, low WTP and no WTP premium are respectively 0.51, 0.27, 0.11 and 0.12. Based on the results of this study, we recommend that Ghana’s NHIS should institute a progressive premium regime in order to cater for the different needs and financial abilities of clients, thus helping to fill the funding gap.
Olatunji Taofik Arowolo, Matthew Iwada Ekum
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.12

Abstract:
Recently, Nigeria focused on Agriculture as a way to diversify her economy. Crop production, which is a proxy to measure agricultural output is considered very important. So, controlling crop production (output) among states in Nigeria is very key. In this study, the generalized regression control chart was used rather than the conventional control chart. The conventional control chart does not put into consideration factor(s) that affect crop production. The generalized regression control chart considers the factor (independent variable) that affect crop production (dependent variable). The normal distribution is a special case of the generalized regression control chart. The possibility of using Weibull regression and other non-normal models were considered. In this research, Gaussian distribution was used as the underlying distribution because it fitted the crop production data. The cost of seed/seedling was selected from a set of independent variables, because it is most significant among other independent variables. The data were collected from secondary sources, precisely National Bureau of Statistics (NBS). All the 36 states in Nigeria, including the Federal Capital Territory (FCT) were involved in the study. The result of the generalized regression control chart showed that crop production is not in control in Nigeria, which was traced to assignable cause of variation in FCT, Abuja. This implied that FCT, Abuja produced below the lower control limit of crop production, despite the relative cost of seed/seedlings.
Yahaya Haruna Umar, Bamanga Muhammad, Udeme Omoren
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.14

Abstract:
West African countries have suffered so much under poor economic growth rate; this issue is largely caused as a result of the poor performance in international trade, which is one of the most essential tools for economic growth. The study investigated the factors influencing international trade in West African Sub region. A Pooled OLS, Fixed Effect Model, and Random Effect Model were adopted to fit the panel regression model for the panel data sets. The study divided the models into three in other to have proper view of factors influencing international trade across West African Sub-region, each model contain the same independent variables and different dependent variables. The result shows that fixed effect model was accurate for the study. Also from the study it was observed that for the first model which use import as dependent variable, gross domestic production, foreign direct investment, and exchange rate are positively significant to import which implies that all the regressor variable influence import across west African sub region positively, while only GDP and FDI are positively significant to export and only FDI is positively significant to trade balance (TB). We therefore conclude that foreign direct investment is the key macro-economic variable that positively influences the policy of international trade across West African over the period of consideration.
Abdushukurov Abdurahim Ahmedovich, Bozorov Suxrob Baxodirovich
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.19

Abstract:
In such areas as bio-medicine, engineering and insurance researchers are interested in positive variables, which are expressed as a time until a certain event. But observed data may be incomplete, because it is censored. Moreover, the random variables of interest (lifetimes) and censoring times can be influenced by other variable, often called prognostic factor or covariate. The basic problem is the estimation of survival function of lifetime. In this article we propose three asymptotical equivalent estimators of survival function in partially informative competing risks model. This paper deals with the estimation of a survival function with random right censoring and dependent censoring mechanism through covariate. We extend exponential – hazard, product - limit and relative - risk power estimators of survival functions in partially informative censoring model in which conditional on a covariate, the survival and censoring times are assumed to be independent. In this model, each observation is the minimum of one lifetime and two censoring times. The survival function of one of these censoring times is a power of the survival function of the lifetime. The distribution of the other censoring time has no relation with the distribution of the lifetime (non-informative censoring). For estimators we show their uniform strong consistency and convergence to same Gaussian process. Comparisons of estimators with the Jensen-Wiedmann’s estimator are included.
Adepeju Opaleye, Oladunni Okunade, Taiwo Adedeji, Victor Oladokun
American Journal of Theoretical and Applied Statistics, Volume 9; https://doi.org/10.11648/j.ajtas.20200904.16

Abstract:
This is an empirical study on the application of SPC techniques for monitoring and detecting variation in the quality of locally produced tobacco in Nigeria. The result provides base evidence for intervention in the quality behavior of the heavily automated tobacco production process in which slight undetected deviation can result in significant wastes. An observational study was carried out within the primary manufacturing department of the tobacco company. The study analysis was conducted using descriptive statistics, goodness of fit test and SPC charts.. These charts were constructed and examined for significant variation in expected output quality as well as the capability of the process. The goodness of fit test and SPC identified CTQs that were approximately normally distributed and out of process control across periods of observations. These deviations were not evident with the summary data or its presentation on the histogram. Subsequently, the out of control process charts were transformed to in-control charts by repetitive elimination of out-of-control instances. At this state, it was observed that the process was only capable of meeting specification for the dust level for all capability measures. These results illustrate a proof of SPC for process monitoring and product quality improvement.
Alebachew Abebe
American Journal of Theoretical and Applied Statistics, Volume 8; https://doi.org/10.11648/j.ajtas.20190805.12

Abstract:
Instructors’ publication (IP) is one of a major activity in higher education institutes. Currently, IP faced problem both high prevalence and severity in Ethiopian public universities. Publication was affected approximately around 352 (73.9%) instructors have not done publication in Ethiopian public universities even if there is a problem in both developing and developed countries. Since, the outcomes from IP factors are mostly discrete variable; they are often modeled using advanced count regression models. It is therefore, the purpose of this study was to determine the appropriate count regression model that efficiently fit the IP data and further to identify the key risk factors contributing significantly to IP in public universities in Ethiopian. The data were collected between November 2015 through November 2016 from selected thirteen (13) public universities in Ethiopian through both questionnaires and interview. A cross sectional study design was employed using IP data. A simple random sampling technique was applied to the population of Ethiopian public universities to obtain a sample of 13 universities or 476 individual instructors were selected. The average age of the 476 participants were found to be 30 years with 31 (6.5%) being females and 445 (93.5%) being males. The count outcomes obtained were modeled using count regression models which included Poisson, Negative Binomial, Zero-Inflated Negative Binomial (ZINB), Zero-Inflated Poisson (ZIP) and Poisson Hurdle regression models. In order to compare the performance and the efficiency of the listed count regression models with respect to the IP data, the various model selection methods such as the Vuong Statistic (V) and Akaikes Information Criterion (AIC) were used. The ZINB count regression model with reference to the values of the Vuong Statistic and AIC were selected as the most appropriate and efficient count regression model for modeling IP data. Based on the ZINB model the variables age, experience, average work-load, association member and motivation to work were statistically significant risk factors contributing to IP in Ethiopian public universities.
Troon John Benedict, Karanjah Anthony, Alilah Anekeya David
American Journal of Theoretical and Applied Statistics, Volume 8; https://doi.org/10.11648/j.ajtas.20190805.13

Abstract:
Measures of dispersion are important statistical tool used to illustrate the distribution of datasets. These measures have allowed researchers to define the distribution of various datasets especially the measures of dispersion from the mean. Researchers and mathematicians have been able to develop measures of dispersion from the mean such as mean deviation, variance and standard deviation. However, these measures have been determined not to be perfect, for example, variance give average of squared deviation which differ in unit of measurement as the initial dataset, mean deviation gives bigger average deviation than the actual average deviation because it violates the algebraic laws governing absolute numbers, while standard deviation is affected by outliers and skewed datasets. As a result, there was a need to develop a more efficient measure of variation from the mean that would overcome these weaknesses. The aim of this paper was to model a geometric measure of variation about the population mean which could overcome the weaknesses of the existing measures of variation about the population mean. The study was able to formulate the geometric measure of variation about the population mean that obeyed the algebraic laws behind absolute numbers, which was capable of further algebraic manipulations as it could be used further to estimate the average variation about the mean for weighted datasets, probability mass functions and probability density functions. Lastly, the measure was not affected by outliers and skewed datasets. This shows that the formulated measure was capable of solving the weaknesses of the existing measures of variation about the mean.
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