Iowa GED Statistical Report, 2004

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar year 2004.

Iowa GED Statistical Report, 2002

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar year 2002

Iowa GED Statistical Report, 2006

The following information summarizes the major statistical trends relative to Iowa’s GED testing program for calendar Year 2005.

Biotic and abiotic variables show little redundancy in explaining tree species distributions

Abiotic factors such as climate and soil determine the species fundamental niche, which is further constrained by biotic interactions such as interspecific competition. To parameterize this realized niche, species distribution models (SDMs) most often relate species occurrence data to abiotic variables, but few SDM studies include biotic predictors to help explain species distributions. Therefore, most predictions of species distributions under future climates assume implicitly that biotic interactions remain constant or exert only minor influence on large-scale spatial distributions, which is also largely expected for species with high competitive ability. We examined the extent to which variance explained by SDMs can be attributed to abiotic or biotic predictors and how this depends on species traits. We fit generalized linear models for 11 common tree species in Switzerland using three different sets of predictor variables: biotic, abiotic, and the combination of both sets. We used variance partitioning to estimate the proportion of the variance explained by biotic and abiotic predictors, jointly and independently. Inclusion of biotic predictors improved the SDMs substantially. The joint contribution of biotic and abiotic predictors to explained deviance was relatively small (similar to 9%) compared to the contribution of each predictor set individually (similar to 20% each), indicating that the additional information on the realized niche brought by adding other species as predictors was largely independent of the abiotic (topo-climatic) predictors. The influence of biotic predictors was relatively high for species preferably growing under low disturbance and low abiotic stress, species with long seed dispersal distances, species with high shade tolerance as juveniles and adults, and species that occur frequently and are dominant across the landscape. The influence of biotic variables on SDM performance indicates that community composition and other local biotic factors or abiotic processes not included in the abiotic predictors strongly influence prediction of species distributions. Improved prediction of species' potential distributions in future climates and communities may assist strategies for sustainable forest management.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, December 2006

A-1 Monthly Public Assistance Statistical Report Family Investment Program.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, January 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program for January 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, February 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program - February 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, March 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - March 2007

Aitchison Geometry for Probability and Likelihood as a new approach to mathematical statistics

The Aitchison vector space structure for the simplex is generalized to a Hilbert space structure A2(P) for distributions and likelihoods on arbitrary spaces. Centralnotations of statistics, such as Information or Likelihood, can be identified in the algebraical structure of A2(P) and their corresponding notions in compositional data analysis, such as Aitchison distance or centered log ratio transform.In this way very elaborated aspects of mathematical statistics can be understoodeasily in the light of a simple vector space structure and of compositional data analysis. E.g. combination of statistical information such as Bayesian updating,combination of likelihood and robust M-estimation functions are simple additions/perturbations in A2(Pprior). Weighting observations corresponds to a weightedaddition of the corresponding evidence.Likelihood based statistics for general exponential families turns out to have aparticularly easy interpretation in terms of A2(P). Regular exponential families formfinite dimensional linear subspaces of A2(P) and they correspond to finite dimensionalsubspaces formed by their posterior in the dual information space A2(Pprior).The Aitchison norm can identified with mean Fisher information. The closing constant itself is identified with a generalization of the cummulant function and shown to be Kullback Leiblers directed information. Fisher information is the local geometry of the manifold induced by the A2(P) derivative of the Kullback Leibler information and the space A2(P) can therefore be seen as the tangential geometry of statistical inference at the distribution P.The discussion of A2(P) valued random variables, such as estimation functionsor likelihoods, give a further interpretation of Fisher information as the expected squared norm of evidence and a scale free understanding of unbiased reasoning

A-1 Monthly Public Assistance Statistical Report Family Investment Program, April 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - April 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, May 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - May 2007

The world distribution of income (estimated from individual country distributions)

We estimate the world distribution of income by integrating individualincome distributions for 125 countries between 1970 and 1998. Weestimate poverty rates and headcounts by integrating the density functionbelow the $1/day and $2/day poverty lines. We find that poverty ratesdecline substantially over the last twenty years. We compute povertyheadcounts and find that the number of one-dollar poor declined by 235million between 1976 and 1998. The number of $2/day poor declined by 450million over the same period. We analyze poverty across different regionsand countries. Asia is a great success, especially after 1980. LatinAmerica reduced poverty substantially in the 1970s but progress stoppedin the 1980s and 1990s. The worst performer was Africa, where povertyrates increased substantially over the last thirty years: the number of$1/day poor in Africa increased by 175 million between 1970 and 1998,and the number of $2/day poor increased by 227. Africa hosted 11% ofthe world s poor in 1960. It hosted 66% of them in 1998. We estimatenine indexes of income inequality implied by our world distribution ofincome. All of them show substantial reductions in global incomeinequality during the 1980s and 1990s.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, June 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - June 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, July 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - July 2007

A-1 Monthly Public Assistance Statistical Report Family Investment Program, August 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - August 2007