Scholarly works
Permanent URI for this collectionhttps://repository.ui.edu.ng/handle/123456789/422
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Item Factors affecting learning in an open and distance learning programme(2010) Dontwi, I. K.; Amahia, G. N.; Chukwu, A. U.; Udomboso, C. G.There is bound to be a shift towards those courses that will provide the knowledge and skills for economic relevance and earning power. Commerce, science and technology are likely to be oversubscribed, once driven world, seems to be diminishing steadily. When designing instruction for distance education, attention is often focused on the cognitive domain, as it is in "traditional" (face-to-face) instruction. What do the students need to know? Which instructional strategies will be most appropriate? Upon what performance criteria will learners be evaluated?Item Alternative goodness-of-fit test in logistic regression models(Medwell Journals, 2011) Nja, M. E.; Enang, E. I.; Chukwu, A. U.; Udomboso, C. G.The Deviance and the Pearson chi-square are two traditional goodness-of-fit tests in generalized linear models for which the logistic model is a special case. The effort involved in the computation of either the Deviance or Pearson chi-square statistic is enormous and this provides a reason for prospecting an alternative goodness-of-fit test in logistic regression models with discrete predictor variables. The Deviance is based on the log likelihood function while the Pearson chi-square derives from the discrepancies between observed and predicted counts. Replacing observed and predicted counts with observed proportions and predicted probabilities, respectively in a cross-classification data arrangement, the standard error of estimate is proposed as an alternative goodness-of-fit test in logistic regression models. The illustrative example returns favourable comparisons with Deviance and the Pearson chi-square statistics.Item The use of gamma and weibull distributions in modeling rainfall data in Nigeria (a comparative analysis)(2010) Udomboso, C. G.; Chukwu, A. U.; Nja, M. E.The distribution of rainfall in Nigeria is not uniform due to the slight differences in the climatic conditions from one geographical region to the other. The climatic conditions ranges from the ‘very wet’ mangrove forest zone of the coastal areas, especially the South-South, to the semi arid regions of the North- West and North-East that share boundaries with the ‘very hot’ desert zone of the North Africa subcontinent. Rainfall data are examples of environmental data, which generally can be modeled by the family of exponential distributions. The Weibull probability function is the most widely used in fitting the distribution of rainfall. This study compares the results obtained by this function with another distribution proposed to the African scientists, that is, the gamma probability function by employing the Kolmogorov- Smimov (K-S) one sample test in testing the goodness- of-fit of these distributions. The results provided are useful tools for decision makers in hydrological and related establishments.Item Effect of attendance on performance in postgraduate courses in science and engineering(International Centre for Mathematical & Computer Sciences, Lagos, Nigeria., 2012) Udomboso, C. G.; Falode, A. O.; Chukwu, A. U.Most postgraduate students in developing countries like Nigeria are working class students that have to shuttle between their workplaces and classes. Reason being that there few or no sponsors for postgraduate programmes exist in the country. Therefore, most students are self sponsored. Furthermore, most postgraduate courses in Nigeria are full time programmes. There are some core courses that require strict attendance in classes. However, it is seen that this is not always the case as the students have to attend to their jobs as well; otherwise they might lose the jobs and have no fund to continue. The programmes considered in this study are those in Statistics and Petroleum Engineering. This study therefore looks at the effect of students’ attendance in postgraduate classes to their performances, and also proffers solutions to its long-term effects on the industrial and economic developments.Item Effect of attendance on performance in postgraduate courses in science and engineering(International Centre for Mathematical & Computer Sciences, Lagos, Nigeria., 2012) Udomboso, C. G.; Falode, A. O.; Chukwu, A. U.Most postgraduate students in developing countries like Nigeria are working class students that have to shuttle between their workplaces and classes. Reason being that there few or no sponsors for postgraduate programmes exist in the country. Therefore, most students are self sponsored. Furthermore, most postgraduate courses in Nigeria are full time programmes. There are some core courses that require strict attendance in classes. However, it is seen that this is not always the case as the students have to attend to their jobs as well; otherwise they might lose the jobs and have no fund to continue. The programmes considered in this study are those in Statistics and Petroleum Engineering. This study therefore looks at the effect of students’ attendance in postgraduate classes to their performances, and also proffers solutions to its long-term effects on the industrial and economic developments.Item Statistical and neural network approach for estimating monthly evapotranspiration at the international institute of tropical agriculture, Ibadan, Nigeria - a comparative study(2011) Chukwu, A. U.; Udomboso, C. G.; Onafeso, O.Evapotranspiration (ET) is one of the main components of the hydrological cycle as it accounts for more than two-thirds of the precipitation losses at the global scale. Reliable estimates of actual Evapotranspiration are crucial for effective watershed modelling and water resource management, yet direct measurements of the Evapotranspiration losses are difficult and expensive. The major objective of this study was to investigate the potential of the classical linear regression and neural network (NN) technique to estimate evapotranspiration, and to examine if a trained neural network with limited input variables can estimate ET efficiently. The study utilized daily climatic data of temperature, relative humidity, sunshine hours, wind speed, and rainfall for ten years collected from the International Institute of Tropical Agriculture. (IITA) Ibadan, Nigeria. Linear regression models in terms of the climatic parameters influencing the regions and, optimal neural network architectures considering these climatic parameters as inputs were developed. The linear regression models showed a satisfactory performance in the monthly estimation in the region selected for the present study. The NN models, however, consistently showed a slightly improved performance over linear regression models. The results also indicated that even with limited climatic variables an ANN can estimate ET accurately.