| 1 |
Author(s):
Etaga Harrison O., Igwebuike Emeka K., Etaga Cecilia N.
Page No : 1-24
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Time Series Models of Crude Oil Production and Export in Nigeria (1999-2015)
Abstract
This study discussed the time series models of crude oil production and export in Nigeria from January 1999- December 2015. The Augmented Dickey-Fuller unit root test employed in the analysis to test for stationarity of the two series indicated that the crude oil production series was stationary at no differencing while crude oil exportation was stationary after first order differencing. The ACF and PACF of both crude production and export identified possible model for both series. Based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) the best model for Crude Production and Export were SARIMA (1,0,1) (2,0,0)12 and SARIMA (2,1,0) (1,0,1)12. Residual analysis and Box-Ljung test proved the adequacy of the models for both series.
| 2 |
Author(s):
Etaga Harrison O., Etaga Cecilia N., Osuoha Chizoba S..
Page No : 25-34
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Effect of Varying Correlation Values on the Efficiency of some Selected Population Variance Estimator using known Values of an Auxiliary Variable
Abstract
The use of Auxiliary variable in estimating the parameters of a study variable has been on the increase. Several authors have worked on some estimator combining various population values of the auxiliary variable and the study variable. There is need to compared some of these estimators under varying correlation values. Twelve estimators were considered. Two distributions were used to do the comparison. Three correlation levels were considered. The performance criteria used is the Mean Square Error (MSE). A sample of size 30 was simulated. The results showed that estimator T4 is the best under Geometric distribution but the worst under the uniform distribution. Estimator T12 is the best under the Uniform while it took 7th in Geometric distribution. Estimators T1, T3, T4, T8, T9, T10 and T12 under the Geometric distribution are not affected by correlation while T2, T5, T6 and T7 were affected by correlation. Under the Uniform distribution, only estimator T3 and T11 had a little effect at high correlation. All other estimators are not affected by correlation. Almost all the estimators are affected by distribution except T1.
| 3 |
Author(s):
Nwankwo Chike H, George Ekemini U.
Page No : 35-53
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Optimization of a Network of Queues in a University Teaching Hospital
Abstract
This paper is aimed at studying the waiting and service times of patients at the Medical Out-Patients Department (MOPD) of the University of Uyo Teaching Hospital, Akwa-Ibom State, Nigeria. The MOPD of the hospital consists of three Units – the Card Room Section, the Vital Signs Unit and the Consulting Rooms. There is a single-server-single-queue system at the Card Room; Two-server-single-queue at the Vital Signs Unit and a seven-parallel single-server queues at the Consulting Rooms. These, put together, form a network of queues. Apart from the inter-arrival times at the Card Room, the inter-arrival times at other sections as well as the service times in all followed distributions other than exponential, and these resulted in models that made use of approximation techniques. Different models were hypothesized at the Vital Signs Unit and the Consulting Rooms, and were combined to see which combination will be most effective at reducing the waiting times of patients in the MOPD. It was realized that a combination of a two-server-single-queue at the Vital Signs Unit and a seven-server-single-queue at the Consulting Room resulted in the least mean waiting times of patients in the MOPD, with the mean waiting time in the queue and in the system being 22 minutes, 15 seconds and 38 minutes, 18 seconds respectively.
| 4 |
Author(s):
Nwankwo Chike H., Onah Jude Chinedu.
Page No : 54-67
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Comparison of Methods of Estimating Missing Values in Randomized Complete Block Designs (RCBD) for Various Number of Missing Values
Abstract
In virtually all types of research, missing value(s) are common occurrences. Several methods and techniques are available for handling this issue. This study aims at comparing the result of using some of the techniques (Do nothing, List-wise Deletion, Overall Mean Imputation, Group Mean Imputation and Least squares Imputation techniques) in handling missing values in Randomized Complete Block Designs (RCBDs). Research data on outcome of an experiment conducted in July 2010 in the department of Animal Science, University of Nigeria Nsukka was employed. The data is made up of a random sample of size 36, on the effect of stocking Densities on the weight of birds at varying ages. Weight gain was used as parameter for measurement. Missing value(s) were introduced into the original complete data set randomly in three different levels of n = 1, 2 and 3. A two-way analysis was carried out, when the data is complete and when there are missing value(s) using the different methods of handling missing values considered. Results showed that the model assumptions of the RCBD was the same, both when the data is complete and when using the different methods of handling missing value(s) employed at different levels of n = 1,2 and 3 missing value(s). There is significant effect of the stocking densities when the data is complete and when using the different method of handling missing value(s) at different levels of n = 1, 2 and 3 missing values. On the contrary, there are significant differences between the MSE of the analysis with the complete dataset and when the different methods of handling missing value(s) at different levels (n=1, 2 and 3) of missingness are created. The complete data showed an MSE of 0.538. On the other hand, for n=1, 2 and 3 missing values respectively, the “Do Nothing” technique generated MSEs of 0.559, 0.573 and 0.577 (average 0.570). Listwise Deletion showed MSEs of 0.564, 0.534 and 0.714, (average 0.604). Random Mean Imputation showed MSEs of 0.537, 0.629 and 0.776, (average 0.657). Group Mean Imputation generated MSEs of 0.553, 0.704 and 0.785, (average 0.681). Least Squares Imputation produced MSEs of 0.553, 0.527 and 0.527, (average 0.530). Hence, the Least Squares Imputation, with consistently small MSEs and the closest average MSE to the true MSE, is recommended among the methods studied.
| 5 |
Author(s):
Oti Eric Uchenna, Onyeagu Sidney Iheanyi.
Page No : 68-78
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Some Versions of K-means Clustering Method and its Comparative Study in Low and High Dimensional Data
Abstract
In this paper we present some versions of k-means clustering method and compare the methods using simulated data, and also low and high dimensional data set in terms of their accuracy and minimized total intra-cluster variance. The versions of k-means clustering method discussed in this paper are namely: The Forgy’s method, Lloyd’s method, MacQueen’s method, Hartigan and Wong’s method, Likas’ method and Faber’s method. These methods minimize a given criterion by iteratively relocating points between clusters until a locally optimal partition is attained. In a basic iterative algorithm, such as k-means, convergence is local and the globally optimum solution cannot be guaranteed. From experimental results, it was observed that Likas’ method and Faber’s method performed better in our synthetic data; method like Likas’ performed better in low dimensional data (iris data) while Hartigan and Wong’s method did better in high dimensional data (yeast cell cycle data).
