Probability Distributions for Showering and Bathing Water-Use Behavior for Various U.S. Subpopulations

It has been shown that bathroom-type water uses dominate personal exposure to water-borne contaminants in the home. Therefore, in assessing exposure of specific population groups to the contaminants in the water, understanding population water-use behavior for bathroom activities as a function of demographic characteristics is vital to realistic exposure estimates. In this article, shower and bath frequencies and durations are analyzed, presented, and compared for various demographic groups derived from analyses of the National Human Activities Pattern Survey (NHAPS) database and the Residential End Uses of Water Study (REUWS) database as well as from a review of current literature. Analysis showed that age and level of education significantly influenced shower and bath frequency and duration. The frequency of showering and bathing reported in NHAPS agreed reasonably well with previous studies; however, durations of these events were found to be significantly longer. Showering frequency reported in REUWS was slightly less than that reported for NHAPS; however, durations of showers reported in REUWS are consistent with other studies. After considering the strengths and weaknesses of each data set and comparing their results to previous studies, it is concluded that NHAPS provides more reliable frequency data, while REUWS provides more reliable duration data. The shower- and bath-use behavior parameters recommended in this article can aid modelers in appropriately specifying water-use behavior as a function of demographic group in order to conduct reasonable assessments of exposure to contaminants that enter the home via the water supply.     

Safety first?

The Royal Statistical Society working party on medical studies on human volunteers made its report on March 13th this year, a year to the day after the disastrous TGN1412 trial left six volunteers gravely ill. Stephen Senn , who chaired the working party, reflects.     

Human resource management and performance

The study of human resource management (HRM) has been invigorated by the promise that there is a best-practice, high-involvement management (HIM) that can guarantee superior organizational performance. None the less, there remain concerns that contingency theory still rules, that is, that the fit between the human resource systems and their context, and particularly the organization's business strategy, is all important and, thus, that HIM will only outperform other systems in certain circumstances. In the 1990s, there has been a spate of research that has sought to test whether HIM is indeed universally relevant. This paper reviews these studies. The paper first introduces the conceptual dimensions of the debate concerning HRM and performance. This shows that the issues go beyond a simple competition between universalism and contingency theory. There are more complicated hypotheses linking human resource practices beneath the surface of the recent literature. The second part of the paper overviews the studies in the light of these hypotheses, revealing that they present an uneven picture. Firstly, there are conceptual differences underlying the studies and, secondly, the results vary between them, and the effects of HIM vary between performance measures even in particular studies. Though a fair number of the studies claim to support universalism, their claims are not always unequivocally supported by their research evidence, and it is premature to conclude in its favour. If anything, there is more support for the 'lean production' argument that stresses the interaction effect between HIM and total quality management on performance.     

Social choice,optimal inference and figure skating

We approach the social choice problem as one of optimal statistical inference. If individual voters or judges observe the true order on a set of alternatives with error, then it is possible to use the set of individual rankings to make probability statements about the correct social order. Given the posterior distribution for orders and a suitably chosen loss function, an optimal order is one that minimises expected posterior loss. The paper develops a statistical model describing the behaviour of judges, and discusses Markov chain Monte Carlo estimation. We also discuss criteria for choosing the appropriate loss functions. We apply our methods to a well-known problem: determining the correct ranking for figure skaters competing at the Olympic Games.     

A century of t-tests

One hundred years ago, an author under the pseudonym of "Student" published a paper which was to become famous. It was entitled "The probable error of a mean". But what we now know as Student's t -test attracted little attention. It took another statistician of genius, R. A. Fisher, to amend, publicise and make it ubiquitous. But both Student's and Fisher's published versions were based upon faulty data. Stephen Senn reminds us of the third dedicated researcher and the quarter of a century delay before the story behind Student's t -test emerged.     

关于f~((k))值分布的正规定则

讨论了全纯函数的正规性问题,由f'(z)值分布的正规定则推广到f(k)(z)值分布的正规定则.     

A DIAGNOSTIC ROUTINE FOR THE DETECTION OF CONSEQUENTIAL HETEROGENEITY OF CAUSAL EFFECTS

Least squares regression estimates of causal effects are conditional-variance-weighted estimates of individual-level causal effects. In this paper, we extract from the literature on counterfactual causality a simple nine-step routine to determine whether or not the implicit weighting of regression has generated a misleading estimate of the average causal effect. The diagnostic routine is presented along with a detailed and original demonstration, using data from the 2002 and 2004 waves of the Education Longitudinal Study, for a contested but important causal effect in educational research: the effect of Catholic schooling, in comparison to public schooling, on the achievement of high school students in the United States.     

MEASUREMENT ERROR IN STYLIZED AND DIARY DATA ON TIME USE

We investigate the nature of measurement error in time-use data. Analysis of "stylized" recall questionnaire estimates and diary-based estimates of housework time from the same respondents of a British survey gives evidence of systematic biases in the stylized estimates and large random errors in both types of estimates. We examine the effect of these measurement problems on three common types of statistical analyses in which the time-use variable is used as: (1) a dependent variable, (2) an explanatory variable, and (3) a basis for cross-tabulations. We develop methods to correct the biases induced by these measurement errors.