Journal of Mathematics and Statistics Studies

Journal Information
EISSN : 2709-4200
Total articles ≅ 15

Latest articles in this journal

Lawrence Chizoba Kiwu, Desmond Chekwube Bartholomew, Fidelia Chinenye Kiwu-Lawrence, Chukwudi Paul Obite, Okafor Ikechukwu Boniface
Journal of Mathematics and Statistics Studies, Volume 2, pp 16-24;

Rotatability property for the Small Box-Behnken design is discussed in this paper. This paper aimed at applying a measure of obtaining percentage rotatability on the Small Box-Behnken designs to determine if the Small Box-behnken designs are rotatable or not and investigated the extent of rotatability in terms of percentage. The factors, q, considered range from 3 to 11. The results showed that for factors q, the Small Box-Behnken design is rotatable for q = 3 factors, near rotatable for q = 4, 7 factors and not rotatable for q = 5, 6, 8, 9, 10 and 11 factors.
Mohammed Al-Ghmadi, Ezz Abdelfattah, Ahmed Ezz
Journal of Mathematics and Statistics Studies, Volume 2, pp 36-49;

The main core of Structural Equation Modeling (SEM) is the parameter estimation process. This process implies a variance-covariance matrix Σ that is close as possible to the sample variance-covariance matrix of data input (S). The six Sigma survey uses ordinal (rank) values from 1 to 5. There are several weighted correlation coefficients that overcome the problems of assigning equal weights to each rank and provide a locally most powerful rank test. This paper extends the SEM estimation method by adding the ordinal weighted techniques to enhance the goodness of fit indicators. A two data sets of the Six Sigma model with different statistics properties are used to investigate this idea. The weight 1.3 enhances the goodness of fit indicators with data set that has a negative skewness, and the weight 0.7 enhances the goodness of fit indicators with data set that has a positive skewness through treating the top-rankings.
Muna H. Ali
Journal of Mathematics and Statistics Studies, Volume 2, pp 50-59;

This study examined the factors affecting the academic achievement of undergraduate students at the faculty of Arts and Science Kufrah -Benghazi University as a case study. This study seeks to identify and analyze some determining factors that influence students' academic achievement in the study area. Four factors namely: psychological, family, learning facilities, and economic; were considered. The sample was randomly selected from the study population. A questionnaire was administered to 240 (90 males,150 females) students as a sample of this study. The responses of the students were analyzed to meet the objectives of the study using (SPSS) and displayed in forms and tables. After collecting the required data on the variables of the study, they were encoded to be entered into the computer to extract the statistical results. Statistical methods within the Statistical Package for Social Sciences (SPSS) were used to process data obtained by the field study of the sample. To analyze the data mean difference test is used. Results of arithmetic means of the psychological, family, learning facilities, and economic factors were medium. Furthermore, there were no statistically significant differences in the factors affecting academic achievement among the participants due to some demographic factors such as gender and marital status. following recommendations were made; provide proper learning facilities to the students and also improve the environment of the faculty. Furthermore, the students would perform well if they are properly guided by both their parents and teachers.
, Madjda Amrani
Journal of Mathematics and Statistics Studies, Volume 2, pp 01-07;

In this work, we study the famous model of volatility; called model of conditional heteroscedastic autoregressive with mixed memory MMGARCH for modeling nonlinear time series. The MMGARCH model has two mixing components, one is a GARCH short memory and the other is GARCH long memory. the main objective of this search for finds the best model between mixtures of the models we made (long memory with long memory, short memory with short memory and short memory with long memory) Also, the existence of its stationary solution is discussed. The Monte Carlo experiments demonstrate we discovered theoretical. In addition, the empirical application of the MMGARCH model (1, 1) to the daily index DOW and NASDAQ illustrates its capabilities; we find that for the mixture between APARCH and EGARCH is superior to any other model tested because it produces the smallest errors.
Mamadou Cisse, Aliou Diop, Souleymane Bognini,
Journal of Mathematics and Statistics Studies, Volume 2, pp 06-15;

In extreme values theory, there exist two approaches about data treatment: block maxima and peaks-over-threshold (POT) methods, which take in account data over a fixed value. But, those approaches are limited. We show that if a certain geometry is modeled with stochastic graphs, probabilities computed with Generalized Extreme Value (GEV) Distribution can be deflated. In other words, taking data geometry in account change extremes distribution. Otherwise, it appears that if the density characterizing the states space of data system is uniform, and if the quantile studied is positive, then the Weibull distribution is insensitive to data geometry, when it is an area attraction, and the Fréchet distribution becomes the less inflationary.
, Ugochinyere Ihuoma Nwosu, Prince Henry Osuagwu, Obioma Gertrude Onukwube
Journal of Mathematics and Statistics Studies, Volume 2, pp 25-35;

The British Pound Sterling (GBP) to Nigerian Naira (NGN) exchange rate has been grossly affected by the Coronavirus 2019 (Covid-19) pandemic. It has become pertinent to identify robust models that will help to cope with the variability associated with the pandemic. Many original studies found the ARIMA method to be highly useful in modeling and forecasting exchange rates. However, not much work has been done on modeling the GBP and NGN exchange rate during the covid-19 pandemic using machine learning models. This study focuses on modeling the exchange rate between the GPB and NGN during the period of the Covid-19 pandemic by adopting the process of model comparison using the Artificial Neural Network (ANN), Autoregressive Integrated Moving Average (ARIMA), and Random Forest models to obtain an optimal model and forecasts from the model. Secondary data of the GBP to NGN exchange rate within the period of the Covid-19 pandemic from were used. The two machine learning models (ANN and random forest) performed better than the ARIMA model. The RF, though performed well in the training set, was outperformed in the test set by the ANN model. The ANN model was chosen to model and forecast the GBP and NGN exchange rate during the Covid-19 pandemic. The predicted fall in the GBP to NGN exchange rate to 570 by December 2021 and 575 by September 2022 using the ANN model will have a huge effect on the economy of the country as the country depends largely on imported goods. The Government and policymakers must put in place structural measures that will avoid the looming crisis.
Olalude Gbenga Adelekan, , Amusan Ajitoni Simeon
Journal of Mathematics and Statistics Studies, Volume 2, pp 53-61;

The study introduced a special case of the Poisson-Generalized Gamma empirical Bayes model to survey states in Nigeria with a higher risk of fatal accidents. Monte Carlo error and stationary dynamic trace plots were used to validate model convergence and accuracy of the posterior estimates. The main results included the disease mappings that revealed Ebonyi had the highest risk of road vehicular fatal accidents in Nigeria with a relative risk estimate of 1.4120 while Abuja had the lowest risk with a relative risk estimate 0.5711. In terms of geopolitical region, the risk of road vehicular fatal accident is highest in South-South region with a relative risk estimate of 1.1850 while North-Central had the lowest risk with a relative risk estimate of 0.7846. The study is to aid planned government programs to ameliorate vehicular road carnage in Nigeria.
, Mariel Africa, Mary-Ann Guilleno, Anne Jeannette C. Pamplona, Jennifer Torrefranca, Levi E. Elipane
Journal of Mathematics and Statistics Studies, Volume 2, pp 21-25;

This paper explores Van Hiele’s Model's use in planning the tasks to identify the properties of quadrilaterals. Lesson study, a professional development program that enables teachers to collaborate to improve teaching and learning quality, was utilized to get necessary data needed for the study. The authors aimed to see to what extent Van Hiele’s Model affects the students’ engagement and development of knowledge in the learning of the research topic. Some observations during the research lesson were as follows: 1) retention of prior knowledge on quadrilaterals was little to non-evident to the students 2) most students still use jargons in order to describe the properties of quadrilaterals and 3) most students were not able to showcase skills in measuring lengths and angles in identifying properties of the quadrilaterals. Given these observations, the following recommendations were as follows: 1) continuous integration and use of mathematical tools such as ruler and protractor in teaching different concepts and processes in Mathematics 2) identifying the level of the learners’ readiness based on the Van Hiele’s model to provide appropriate examples and activities in the context of the students 3) providing hands-on activities such as geometric construction and measuring activities that would enhance students’ capabilities in reasoning and proving. Lesson study served as a powerful tool to reflect on the researchers' processes and activities in conducting the study.
Olusegun O. Alabi Kayode Ayinde,
Journal of Mathematics and Statistics Studies, Volume 2, pp 12-20;

Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused on developing estimators regardless of effect of differences in levels of multicollinearity among regressors. Studies have considered single-estimator and combined-estimator approaches without sustainable solution to multicollinearity problems. The possible influence of partitioning the regressors according to multicollinearity levels and extracting from each group to develop estimators that will estimate the parameters of a linear regression model when multicollinearity occurs is a new econometrics idea and therefore requires attention. The results of new studies should be compared with existing methods namely principal components estimator, partial least squares estimator, ridge regression estimator and the ordinary least square estimators using wide range of criteria by ranking their performances at each level of multicollinearity parameter and sample size. Based on a recent clue in literature, it is possible to develop innovative estimator that will sustainably solve the problem of multicollinearity through partitioning and extraction of explanatory variables approaches and identify situations where the innovative estimator will produce most efficient result of the model parameters. The new estimator should be applied to real data and popularized for use.
Urvisha Vaghela, Dharamvirsinh Parmar
Journal of Mathematics and Statistics Studies, Volume 2, pp 26-39;

Let graph G=(V(G),E(G)) attains a Skolem difference mean labeling with p vertices and q edges is said to be an extra Skolem difference mean labeling of graph G if all the labels of the vertices are odd. The graph which attains an extra Skolem difference mean labeling is called an extra Skolem difference mean graph. We obtain an extra Skolem difference mean labeling for Comb graph, Twig of a path P_n, H graph of a path P_n, K_1,2*K_(1,n) graph, K_1,3*K_(1,n) graph, m- Join of H_n, P_n⊙K_(1,m) graph , HSS(P_n) graph, H⊙〖mK〗_1-graph of a path P_n.
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