Competencies in understanding statistical information in primary and secondary school levels : an inter-cultural empirical study in Germany and Colombian students

Resumen en espa??ol, portugu??s y franc??s

Distribución de la información estadística = Distribution of statistical information

Describe los canales de distribucion de informacion estadistica disponibles en la actualidad y presenta un nuevo enfoque que permitiria vincular distribucion y produccion con el uso alternativo de esos posibles canales y que se denomina "distribucion multimedia"; examina la organizacion institucional de la distribucion; y plantea algunas reflexiones e interrogantes con el objetivo de cooperar al debate de la reunion.

Programa de trabajo estadístico internacional para América Latina y el Caribe, 1995-1996: Subprograma 4: difusión de la información estadística = Programme of international statistical work for Latin America and the Caribbean, 1955-1996: Subprogramme 4: dissemination of statistical information

Incluye Bibliografía

The ECLA Programme of Population research and the improvement of statistical information

Includes bibliography

Statistical Information: A Bayesian Perspective

We explore the meaning of information about quantities of interest. Our approach is divided in two scenarios: the analysis of observations and the planning of an experiment. First, we review the Sufficiency, Conditionality and Likelihood principles and how they relate to trivial experiments. Next, we review Blackwell Sufficiency and show that sampling without replacement is Blackwell Sufficient for sampling with replacement. Finally, we unify the two scenarios presenting an extension of the relationship between Blackwell Equivalence and the Likelihood Principle.

Automatic estimation of noise parameters in Fourier-domain optical coherence tomography cross sectional images using statistical information

We present an application and sample independent method for the automatic discrimination of noise and signal in optical coherence tomography Bscans. The proposed algorithm models the observed noise probabilistically and allows for a dynamic determination of image noise parameters and the choice of appropriate image rendering parameters. This overcomes the observer variability and the need for a priori information about the content of sample images, both of which are challenging to estimate systematically with current systems. As such, our approach has the advantage of automatically determining crucial parameters for evaluating rendered image quality in a systematic and task independent way. We tested our algorithm on data from four different biological and nonbiological samples (index finger, lemon slices, sticky tape, and detector cards) acquired with three different experimental spectral domain optical coherence tomography (OCT) measurement systems including a swept source OCT. The results are compared to parameters determined manually by four experienced OCT users. Overall, our algorithm works reliably regardless of which system and sample are used and estimates noise parameters in all cases within the confidence interval of those found by observers.