The association between mood states and physical activity in postmenopausal, obese, sedentary women

Mood states influence evaluative judgments that can affect the decision to exercise or to continue to exercise. This study examined how mood associated with graded exercise testing (GXT) in sedentary, obese, postmenopausal women (N = 25) was associated with physical activity and predicted VO2max during and after a behavioral weight-loss program (BWLP). Measures of physical activity included planned exercise, calories from physical activity, leisure-time physical activity, and predicted VO2max. Mood before and after pre-BWLP GXT was assessed using the Profile of Mood States. Mood before and after the GXT was more strongly associated with planned exercise than other forms of physical activity, and this effect became stronger over time. Mood enhancement in response to exercise was not related to physical activity. Mood before and after exercise might yield important clinical information that can be used to promote physical activity in sedentary adults.     

Accuracy,confidence and consensus in bayesian hypothesis inference

Interest centres on a group of statisticians , each supplied with the same n sample datapoint sandmaking formal Bayesian inference with a common likelihood function but differing prior knowledge and utility functions.

Definitions are proposed which quantify, in a commensurable way, the inference processes of “accuracy”, “confidence” and “consensus” for the case of hypothesis inference with a fixed sample size n.

The general significance of comparing the three quantifiers is considered. As n increases the asymptotic behaviour of the quantifiers is evaluated and it is found that the three rates of convergence are of the same order as a function of n. The results are interpreted and some of their implications are discussed.     

Optimal allocation of time points for the random effects model

In this article the problem of the optimal selection and allocation of time points in repeated measures experiments is considered. D‐ optimal designs for linear regression models with a random intercept and first order auto‐regressive serial correlations are computed numerically and compared with designs having equally spaced time points. When the order of the polynomial is known and the serial correlations are not too small, the comparison shows that for any fixed number of repeated measures, a design with equally spaced time points is almost as efficient as the D‐ optimal design. When, however, there is no prior knowledge about the order of the underlying polynomial, the best choice in terms of efficiency is a D‐ optimal design for the highest possible relevant order of the polynomial. A design with equally‐spaced time points is the second best choice     

Bootstrapping in least absolute value regression: an application to hypothesis testing

A Monte Carlo simulation is used to study the performance of hypothesis tests for regression coefficients when least absolute value regression methods are used. In small samples, the results of the simulation suggest that using the bootstrap method to compute standard errors will provide improved test performance     

More Powerful Tests from Confidence Interval p Values

In this article the problem of comparing two independent binomial populations is considered. It is shown that the test based on the confidence interval p value of Berger and Boos often is uniformly more powerful than the standard unconditional test. This test also requires less computational time.     

Correlation,Regression Lines,and Moments of Inertia

The R language, a freely available environment for statistical computing and graphics is widely used in many fields. This “expert-friendly” system has a powerful command language and programming environment, combined with an active user community. We discuss how R is ideal as a platform to support experimentation in mathematical statistics, both at the undergraduate and graduate levels. Using a series of case studies and activities, we describe how R can be used in a mathematical statistics course as a toolbox for experimentation. Examples include the calculation of a running average, maximization of a nonlinear function, resampling of a statistic, simple Bayesian modeling, sampling from multivariate normal, and estimation of power. These activities, often requiring only a few dozen lines of code, offer students the opportunity to explore statistical concepts and experiment. In addition, they provide an introduction to the framework and idioms available in this rich environment.     

Family Policy and Complex Contemporary Families: A Decade in Review and Implications for the Next Decade of Research and Policy Practice

Prior Journal of Marriage and Family decade-in-review articles have grappled with the definition and role of family policy for research and policy practice while emphasizing its value to both. In this article, we begin with a broad conceptualization of family policy that encompasses actions intended to achieve explicitly stated goals for families (explicit policies) and those that affect families without an explicitly stated goal for doing so (implicit policies), which we believe provides a solid framework for guiding and understanding both research and practice in the field. Second, we review major U.S. policy initiatives in the past decade and their documented and potential effects on families. Third, we describe several key aspects by which contemporary families have become more diverse and complex. Fourth, we discuss the implications of ongoing family complexity for public policies. We conclude with a discussion about future research and policy development in the context of contemporary family complexity.