Browsing by Author "Oshungade, I. O."
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item An Improvement on Some Approximate Solutions to Dalenius Equation(College of Natural Sciences, Al-Hikmah University, Ilorin, Nigeria, 2016) Kareem, A. O.; Oshungade, I. O.; Oyeyemi, G. M.Dalenius researched into the problem of strata boundary determination and came up with some sets of general equations that must be satisfied to reach optimum points of stratification (OPS). These equations met a lot of criticism in terms of its difficulty and time involved in solving them as well as its practical adaptability. Thus, for easy application, sets of approximate solutions were suggested. Deficiencies in the suggested solutions include unavailability of a theory to guide the choice of class interval in their application, use of Approximate Boundary Value (ABV) and overlapping within strata. This study developed the Exact Boundary Value (EBV) approach which places the boundaries at their exact value, eliminates overlapping within strata and produces more strata formation than the ABV. In terms of the precision of the two approaches, the EBV approach was found to be much more precise than the ABV approach for both optimum and proportional allocation.Item Moving Average Stratification Algorithm for Strata Boundary Determination in Skewed Populations(Central Bank of Nigeria (CBN), 2016) Kareem, A. O.; Oshungade, I. O.; Oyeyemi, G. M.; Adejumo, A. O.Moving Average Stratification (MAS) is a new competing and simple algorithm for strata boundary determination in Stratified Sampling. It eliminates arbitrary choice of class interval associated with cumulative square root of frequency method (Dalenius and Hodges Rule (DHR) 1959) and the inherent geometric gaps created within strata by Geometric Stratification (GMS) of Gunning & Horgan (2004). It competes favorably well with DHR and GMS in terms of its precision, simplicity and speeds and therefore recommended for use in strata boundaries determination especially in skewed populations.Item A Note on the Precision of Stratified Systematic Sampling(Scientific Research Publishing, 2015) Kareem, A. O.; Oshungade, I. O.; Oyeyemi, G. M.in stratified sampling in terms of the precision of the population mean base on the inherent characteristics of the population. These conflicting views were analyzed using Cochran data (1977, p.211) [1]. When the population units are ordered, variance of systematic sampling for all possible systematic samples provides equal, non-negative and most precise estimates for all the variance functions considered i.e. 1 ( sy ) 2 ( sy ) 3 ( sy ) V y = V y = V y , unlike when a single systematic sample is used and when variance of simple random sampling is used to estimate selected systematic samplesItem On Linear Stratification of Skewed and Normal Populations(Faculty of Science, University of Ibadan, Nigeria, 2016) Kareem, A. O.; Oshungade, I. O.; Oyeyemi, G. M.the literature in the appraisal of the performance of methods of strata construction which fails to account for the bias associated with each method because the most precise method may not actually be the most efficient. This study develops Linear Stratification (LS) as a new and simple approach to strata boundary determination. Strata boundaries were established with LS, cumulative square root of frequency method and Geometric Stratification. Samples were selected randomly without replacement from each stratum and estimates of the population parameters obtained. These estimates were compared i.e. LS with that of the two existing methods using four sets of real life data with varying degrees of skewness. With the Mean Square Error (MSE) value rather than minimum variance commonly used for appraisal, the results show that LS provides minimum MSE value in both skewed and normal populations, hence the most efficient when compared with the two competing methods in strata boundary determination.Item Rao, Hartley, and Cochran’s Sampling Scheme: Application(Mathematical Theory and Modeling, 2013) Dawodu, O. O.; Adewara, A. A.; Oshungade, I. O.This paper takes a look at the Rao, Hartley, and Cochran’s sampling scheme, when it is required to select sample of sizes 4, 6, 12, and 18 with probability proportional to size without replacement sampling (unequal probability sampling without replacement). This is done by using the data from the 2008 Nigeria Demographic and Health Survey (NDHS). We studied the distribution of women age 15 – 49 years employed in the 12 months preceding the survey by type of employer. Here, we considered self-employed women alone. It is shown how sample of sizes 4, 6, 12, and 18 could be randomly selected from the population of size 36. Population total and variance were computed with confidence interval constructed for the population total. For the randomly selected states in this paper, we realized that as the sample size increases, the variance and standard error decreases.