Less Pain,More Gain

SUMMARY

Applied social statistics is typically one of the most dreaded courses in a social work program. Technological advances in computer applications offer tools to lessen the pain and increase the gains associated with teaching and learning statistics. This paper reports on a modest research project resulting in a computer-based approach to teaching Applied Social Statistics. This approach makes full use of MicroCase, a statistical software package. Results of evaluative efforts which appraised the computer technology-enhanced statistics course are presented. Course format, classroom procedures, workbook and homework assignments, evaluation tools, and teaching strategies are also suggested.     

Order statistics in goodness of fit tests

Goodness-of-fit tests based on the order statistics of a random sample are given which are particularly effective in detecting long tailedness or outliers.     

A model for integrated production-packaging systems

The problem of determining the economic packaging frequency of jointly replenished items has received a lot of attention from researchers. This problem is usually encountered while packaging the products after completing manufacture. Nevertheless, production batch quantities certainly influence the manufacturing cycle time of products and hence the economic packaging quantities for jointly replenished items. Therefore, realizing the importance of the relationship between production batch quantities and packaging quantities     

执行命令行为正当化事由研究

执行命令行为广泛地存在于国家和社会生活之中,是国家权力运作的一种具体表现形式。应结合行为无价值与结果无价值的立场去寻求其正当化根据;并以此为观念指导,重新构建其正当化条件,以实现国家职能,防止国家权力的滥用。     

Recurrence relations for single and product moments of order statistics from a generalized half logistic distribution with applications to inference

In this paper, we derive several recurrence relations satisfied by the single and product moments of order statistics from a generalized half logistic distribution. These generalize the corresponding results for the half logistic distribution established by Balakrishnan (1985). The relations established in this paper will enable one to compute the single and product moments of all order statistics for all sample sizes in a simple recursive manner; this may be done for any choice of the shape parameter k. These moments can then be used to determine the best linear unbiased estimators of location and scale parameters from complete as well as Type-I1 censored samples.     

Use of tail areas for testing goodness-of-fit

Characterizing a set of data as a random sample from a specified distribution is often a precursor to statistical inference or hypothesis testing involving the extremes of the distribution -precisely the regions of greatest uncertainty. It seems reasonable then to exploit as best we can our limited knowledge of this region. Toward this end we investigate here the areas in the tails of the distribution as determined by the extreme order statistics as a criterion for testing goodness-of-fit.     

On fourth order rotatable designs

Recently, Draper and Herzberg posed some open problems on fourth order rotatable designs. It has been attempted to settle these open problems.     

On comparing two poisson intensity functions

Given realizations of two possion processes with unknown intensities A(·) and F(·) observed over the interval (t₁,t₂), we suppose that it is desired to distinution between H₀ Ξ(·)/λ(·) is constant on (t₁,t₂) versus H₊:Ξ(·)/λ(·) increases on (t₁,t₂). We propose a decision rule which uses the percentage points of the Mann-Whitney U-distribution. We show that the decision rule is unbiased and that the set of alternatives in H₊ can be weakly ordered, specifically: if Ξ(·)/λ(·), β(·)/λ(·) and Ξ(·)/β(·) are increasing on (t₁, t₂) then P{H₀ is rejected |Ξ(·)}≧P{H₀ is rejected|B(·)}≧P{H₀ is rejected|H₀}.     

On unbiased estimation of gini index and yntema-pietra index of lognormal distribution and their variances

Here we derive the uniformly minimum variance unbiased (best) estimator and a strongly consistent, asymptotically normal, unbiased estimator of each of Gini index and Yntema-Pietra index of lognormal distribution . These estimators are in terms of generalized hypergeometric functions ₁F₂. Further, the variances of these estimators and the best estimators of variances of best estimators are found out. They are in terms of Kempé de Fériet's hypergeometric functions.