This paper discussed the Application of SARIMA Models in Modeling and Forecasting Nigeria's Inflation Rates. Time series analysis and forecasting is an efficient versatile tool in diverse applications such as in economics and finance, hydrology and environmental management fields just to mention a few. Among the most effective approaches for analyzing time series data, the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA) was employed in this study. In this paper, we used Box-Jenkins methodology to build ARIMA model for Nigeria's monthly inflation rates for the period November 2003 to October 2013 with a total of 120 data points. In this research, ARIMA (1, 1, 1) (0, 0, 1)12 model was developed, and obtained as 1t y + = 0.3587y t +0.6413y t-1 -0.8840e t-11 -0.7308912e t-12 +0.8268e t . This model is used to forecast Nigeria's monthly inflation for the upcoming year 2014. The forecasted results will help policy makers gain insight into more appropriate economic and monetary policy in other to combat the predicted rise in inflation rates beginning the first quarter of 2014.
This paper examined the modeling and forecasting malaria mortality rate using SARIMA Models. Among the most effective approaches for analysing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). In this paper, we employed Box-Jenkins methodology to build ARIMA model for malaria mortality rate for the period January 1996 to December 2013 with a total of 216 data points. The model obtained in this paper was used to forecast monthly malaria mortality rate for the upcoming year 2014. The forecasted results will help Government and medical professionals to see how to maintain steady decrease of malaria mortality in other to combat the predicted rise in mortality rate envisaged in some months.
This paper discussed the longitudinal studies of random effect model on academic performance of student using Federal University of Technology, Owerri Imo State Nigeria as a case study. Secondary data were adopted for the research work, and a SAS software package was used for the analysis. There appears to be some curvature in the average trend and individual profile plots, and hence a quadratic time effect was fitted to the data. From the individual profiles are the total observations collected for the analysis. From the profiles of the type of SSA, Entry Age, Entry Aggregate and Gender, it could be assumed that each profiles evolution follows a quadratic trend. Also, it could be concluded that most students who started with low GPA at semester one, improved in their performance to semester three and there was a downward trend before semester seven. Further, the mean profile for SSA was explored. From the chosen model among all models fitted to the data set, we conclude based on the results obtained that student's GPA depends on the SSA, Entry Age, Entry Aggregate and Gender). Student with high and medium admission aggregates scores high GPA and student with low admission aggregates scores low GPA at semester one, but on the average students with Low and Medium Entry Aggregate score higher GPA than students with High Entry Aggregate. The performance of GSS students is better as compare to that PSS at semester one and on the average. Meanwhile, in all the models it appeared, student GPA's increase from semester one to semester three and decreases after semester three. Generally students tend to perform better at the third semester. The analysis also revealed that the academic performance is dependent on the SSA, Entry Age, Entry Aggregate and Gender.
This study tends to analyze the school examination results (scores) of 300 randomly selected students of Imo State Polytechnic, Umuagwo near Owerri, Imo State, Nigeria who offer English Language and Mathematics as general courses, using the binary logistic regression model with the aim of examining how some factors (variables) in secondary school level contribute to the performance of the students in the Polytechnic. The analysis is performed on the basis of the explanatory variables viz; gender, type of secondary schools, category of secondary schools, board of examinations and location of secondary schools, where scores of students in English Language and Mathematics are assumed to be the response variables. Applying the method of Correspondence Analysis revealed that there exist a significant correlation between board of examinations and location of schools, which made the analysis to be into two stages. The first stage is based on using English Language and Mathematics as a response variable with gender, type of secondary schools, category of secondary schools, and board of examinations as the explanatory variables. The second stage, on the other hand, English Language and Mathematics is the response variable, while gender, type of secondary schools, category of secondary schools, and location of schools are the explanatory variables. The odds ratio analysis compares the scores obtained in two examinations viz English language and Mathematics. The result of the analysis revealed that females are always showing best performances in Mathematics than English examination in all the two stages carried out in this paper. The study also showed that performances of students from girls' schools are found to be the best in English Language course examination than those of students from boys; secondary schools. Furthermore, the study revealed that government schools always show better performance in English course examination than in Mathematics.
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