Statistics

Child care has become an essential component of life in our society .Quality child care can make a significant difference in child’s development .Here we consider some regulations in favour of lower child staff ratio ,higher educational standards for care givers and smaller group size . In this paper we present a methods for fuzzy risk analysis based on, child- staff ratio group size and care giver ability. We obtain the expected number of children in queue needing attention at any time by using both function principle and Graded Mean Integration method. Furthermore we calculate the proportion of time that a child would spend engaged by using both function principle and Graded Mean Integration method. The model presented in this paper can be applied to other educational services also

In this paper, we propose conventional and alternative ratio-product type estimators for population mean using two auxiliary variables in the presence of non-response. The purposed estimators have been found to be more efficient than the relevant estimators for the fixed values of first-phase sample of size n' and sub-sample of size n(<n') taken from the first-phase sample size n' under the specified condition. The purposed estimators are more efficient than the corresponding estimators for population mean (Y ?) of a study variable y in the case of fixed cost and have less cost in comparison to the cost incurred by the corresponding relevant estimators for a specified variance. The conditions under which the purposed estimators are more efficient then the relevant estimator have been obtained. An empirical as well as a Monte-Carlo simulation study have been done to demonstrate the efficiencies for the purposed estimators over other relevant estimators.

In tackling with a big dataset, a new and better approach is crucial to be used for. As in this paper, to develop an algorithm modelling for Boosted Regression Trees (BRT), author are decided to use the programming R statistical data analysis tool. The data used in this research, is a one-hour time range of data collected from 2009 up to 2012 for an environment station located at coastal-environment area somewhere in northern of Malaysia. Thus, step by step flowchart from the beginning till the objective been achieve, were provided, and created. Sensitive testing of model been carried out with the three main parameters. Only the number of trees (nt) is to be determine by using the method of estimating the optimal number of iterations; an independent test set (test), out-of-bag estimation (OOB), and five-fold CV. While the learning rate (lr) and interaction depth (tc) been fixed at 0.001 and 5 respectively. Results indicated that the BRT analysis algorithm best modelled with the best combination of parameters nt of 10000 together with lr and tc that achieves minimum predictive error (minimum error for predictions). Besides, with the boosting output of relative influence plot, and partial dependency plot, the variables significantly influenced Ozone are humidity, ambient temperature, NO, and wind speed with 61.72%, 18.17%, 10.27% and 4.5% respectively. The algorithm model for BRT produced by using the simulated data is best guidance to be used in the field of air pollution specifically. As a matter of fact, the BRT Algorithm can be modelled in varies field with big dataset.

This paper introduces the theory of calibration estimator to ratio estimation in stratified systematic sampling scheme and proposes a class of calibration ratio-type estimators for estimating population mean Y ? of the study variable y using auxiliary variable x. The bias and variance of the proposed estimator have been derived under large sample approximation. Calibration Asymptotic optimum estimator (CAOE) and its approximate variance estimator are derived. An empirical study to evaluate the relative performances of the proposed estimator against members of its class is carried out. Analytical and numerical results proved the dominance of the new proposal.

Information criterion, KIC, for the model selection based on kullback-leibler risk symmetric by cavanagh, be present for large sample. KIC as an asymptotically unbiased estimator kullback-leibler risk symmetric, is consider divergency between the true model and the candidate model. It is an inconsistent information criterion. All the criteria that exsist based on kullback-leibler risk (symmetric or asymmetric)are inconsistent. In this article based on information criterion KIC is defined consistent information criterion, MIC. This criterion, MIC also is made based on kullbake-leibler symmetric. At the end these two information criteria for linear regression models has been consider by simulation monte carlo.

This paper proposes, with justification, a class of product-cum-dual to product estimators for estimating the population mean in systematic sampling using auxiliary information. The bias and variance of the proposed class of estimators have been derived under large sample approximation. Asymptotic optimum estimator (AOE) and its approximate variance estimator are derived and efficiency comparisons made with existing related estimators in theory. Analytical and numerical results show that at optimal conditions, the proposed class of estimators is always more efficient than all existing estimators under review.

Livestock industry in Nigeria is ridden with myriad of problems, which have resulted to a gross shortage of meat and other animal products. The growth rate of agriculture sector in Nigeria is still below the potentials of the natural and human resources due to high cost of agricultural inputs. To increase protein intake in Nigeria, there is urgent need to increase broiler production at household and commercial holdings. This research was conducted to assess the weight gained by the broiler chickens served fluted pumpkin leaves extract and also to examine the possible combination of number of weeks and  quantity of fluted pumpkin leaves extract that can result in maximum weight of the broiler chickens. The data were collected as a secondary data from the Federal College of Animal Health and Production Technology (I.A.R & T), Ibadan. An 8-weeks experiment was conducted to assess the weight gained by the broiler chickens served with fluted pumpkin leaves extract (FPLE). Forty day old Anak 2000 broiler chicks were randomly distributed to 5 treatments which contained 0, 30, 60, 90 and 120 ml of FPLE per litre of water for A, B, C, D and E, respectively, in a completely randomized design. Each treatment was replicated four times with two birds per replicate. The birds were fed with the same starter and finisher diets. The feed and water were served. Factorial design was used to study the main and interaction effects of number of weeks and quantity of FPLE on the weight of broiler chickens. Response surface model was fitted and subjected to canonical analysis to the characterization of the nature of its turning point and to capture the combination of number of weeks and quantity of FPLE that brings maximum weight of the broiler chickens. The results showed that the average body weight gained was significant (P<0.05). It was least in control compared to the birds served with 30-120 ml of FPLE. Factorial Design revealed that birds served with FPLE gained more weight than those in control. The birds served 120 ml of FPLE per litre of water for 8 weeks had the best performance in terms of weight gain. The use of FPLE in broiler chickens production is most effective from five weeks of age. The fitted Response Surface Model indicated that number of weeks and quantity of FPLE together with their mutual interaction significantly (P<0.05) determined the weight of broiler chickens. The maximum weight was achieved when number of week was ten with 100ml of FPLE. Number of weeks, FPLE and their mutual interaction play a key role in obtaining maximum weight of broiler chickens. These factors should be put into consideration in making of feed for broiler chickens.

The choice of the similarity coefficient used in clustering could have great impact on the resulting classification, there is need to study and understand these coefficients better to be able to make the right choice for specific situations. In this study, variations caused by three similarity coefficients: Dice, Jaccard and Simple matching with five clustering methods: (Unweighted Pair-Group Mean Arithmetic (UPGMA), Weighted Pair-Group Mean Arithmetic(WPGMA), complete linkage, single linkage and Neighbour-Joining with AFLP markers in Colletotrichum gloeosporioides isolates from yam were assessed. Comparison among the similarity coefficients and clustering methods were made using correlation analysis, multidimensional scaling and principal component analysis. Dendrogram topology was compared using consensus fork index (CFI) and node counts. The grouping of the pathogens by the markers is not related to their agro-ecological zones. The CFI results showed varying level of similarity for the cluster analysis CA methods. It was observed that high correlation does not necessarily imply similarity in the topology of a tree, therefore care should be taken in its interpretation. The cophenetic correlation with original distances suggests that the UPGMA method gives consistent results with respect to grouping irrespective of the similarity coefficient. The use of UPGMA method is therefore recommended for its consistency.

This paper estimated Foster-Greer-Thorbecke (FGT) poverty indices from the generalized burr density function to further justify the wide flexibility and applicability of the function in fitting many life datasets. It computed estimates of the parameters of the selected density and the goodness of fit statistic from the 2010 Harmonized Nigeria Living Standard Survey (HNLSS) dataset conducted by the National Bureau of Statistics (NBS) of Nigeria. The goodness of fit test indicated that the selected density was appropriate and estimates of the indices obtained from the density were approximately close to the ones obtained through the traditional approach.

This study considered a method of selecting discrete distributions based on the Katz criterion in fitting an appropriate probability distribution function to available crime data in Nigeria after reasonable transformation. The criterion selected the Negative Binomial distribution for the data under consideration. The adopted distribution provided good fit as evidenced by the Anderson Darling goodness of fit test. This study has therefore applied the Katz criterion to real life data.