Breakdowns in Common Statistical and Graphical Techniques for Big Data

Many common statistical techniques, such as hypothesis testing, break down in the presence of massive data sets. What common techniques need alternatives in the face of Big Data

The effect of locational uncertainty in geostatistics

A datum is considered spatial if it contains locational information. Typically, there is also attribute information, whose distribution depends on its location. Thus, error in locational information leads to error in attribute information, which is ultimately reflected in the inference drawn from the data;We propose a statistical model for incorporating locational error into spatial data analysis. We investigate the effect of locational error on the spatial lag, the covariance function, the variogram, and optimal spatial prediction (aka, kriging). We show that the basic methodology of kriging adjusted for locational error is the same as kriging without locational error;We also develop a hierarchical Bayesian model to incorporate locational uncertainty into spatial data analysis. We use Markov chain Monte Carlo techniques to draw from the posterior distribution of the large-scale trend parameters, the covariance-model parameters, the realized site locations, and the process value at a prediction site, given the observed attribute values and the intended sample locations;We use a topographical data set of Davis (1973) as an illustration of kriging without locational error, kriging adjusted for locational error, hierarchical Bayesian kriging without locational error, and hierarchical Bayesian kriging adjusted for locational error. We also investigate, through a simulation study, the effect that varying the trend, the measurement error, the locational error, the range of spatial dependence, the sample size, and the prediction location has on both kriging without locational error and kriging adjusted for locational error

GAISE Into the Future: Updating a Landmark Report for an Increasingly Data-Centric World

Ever since its official endorsement by the American Statistical Association in 2005, the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report has had a profound impact on the teaching of statistics. Now, a decade later, it is important to recognize the changing nature in what and how we teach our introductory statistics students. Changes in technology and assessment practices, just over the past 10 years, have made it possible to do new and exciting things in our courses, in very different ways than were envisioned by the authors of the original GAISE College Report. Further, our world is becoming increasingly data-centric, and it is important to recognize and promote statistics as a way to use data to make decisions. This panel will report on efforts to revise the GAISE College Report in the light of the changes that have occurred within the field of Statistics Education (and other areas) within the past several years. Panelists will also elicit feedback from the audience about specific (and perhaps controversial) issues related to the teaching of introductory statistics at the college level

Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016

In 2005 the American Statistical Association (ASA) endorsed the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report. This report has had a profound impact on the teaching of introductory statistics in two- and four-year institutions, and the six recommendations put forward in the report have stood the test of time. Much has happened within the statistics education community and beyond in the intervening 10 years, making it critical to re-evaluate and update this important report. For readers who are unfamiliar with the original GAISE College Report or who are new to the statistics education community, the full version of the 2005 report can be found at http://www.amstat.org/education/gaise/GaiseCollege_full.pdf and a brief history of statistics education can be found in Appendix A of this new report. The revised GAISE College Report takes into account the many changes in the world of statistics education and statistical practice since 2005 and suggests a direction for the future of introductory statistics courses. Our work has been informed by outreach to the statistics education community and by reference to the statistics education literature

Developing Best Practices in Preparing Statistics PhDs to Teach

Graduate programs in statistics do a commendable job of preparing PhD students to practice the discipline of statistics in industry, government, and academia. Unfortunately, most PhD programs do not adequately prepare a student to become a teacher of statistics at the university level. Students graduate without a working knowledge of best practices in statistics teaching and resources that are available for teaching statistics (particularly at the undergraduate level). This roundtable will be a brainstorming session for developing a set of best practices in mentoring graduate students who plan to teach and in mentoring new teachers of statistics at the university-level. The intended audience includes experienced teachers who have an interest in the preparation of graduate students who will become the next generation of statistics teachers

Publishing Your Statistics Education Research

Many teachers and statistics educators are unaware of the variety of publication outlets for work in statistics education. This session will include editors from the Journal of Statistics Education, Teaching Statistics, Statistics Education Resource Journal, Technology in Statistics Education, Consortium for the Advancement of Undergraduate Statistics Education, and Statistics Teachers Education Web. The editors will discuss how to publish research and resources in their respective venues

The effect of locational uncertainty in geostatistics

A datum is considered spatial if it contains locational information. Typically, there is also attribute information, whose distribution depends on its location. Thus, error in locational information leads to error in attribute information, which is ultimately reflected in the inference drawn from the data;We propose a statistical model for incorporating locational error into spatial data analysis. We investigate the effect of locational error on the spatial lag, the covariance function, the variogram, and optimal spatial prediction (aka, kriging). We show that the basic methodology of kriging adjusted for locational error is the same as kriging without locational error;We also develop a hierarchical Bayesian model to incorporate locational uncertainty into spatial data analysis. We use Markov chain Monte Carlo techniques to draw from the posterior distribution of the large-scale trend parameters, the covariance-model parameters, the realized site locations, and the process value at a prediction site, given the observed attribute values and the intended sample locations;We use a topographical data set of Davis (1973) as an illustration of kriging without locational error, kriging adjusted for locational error, hierarchical Bayesian kriging without locational error, and hierarchical Bayesian kriging adjusted for locational error. We also investigate, through a simulation study, the effect that varying the trend, the measurement error, the locational error, the range of spatial dependence, the sample size, and the prediction location has on both kriging without locational error and kriging adjusted for locational error.</p

Fast, resolution-consistent spatial prediction of global processes from satellite data

Polar orbiting satellites remotely sense the earth and its atmosphere, producing datasets that give daily global coverage. For any given day, the data are many and measured at spatially irregular locations. Our goal in this article is to predict values that are spatially regular at different resolutions; such values are often used as input to general circulation models (GCMs) and the like. Not only do we wish to predict optimally, but because data acquisition is relentless, our algorithm must also process the data very rapidly. This article applies a multiresolution autoregressive tree-structured model, and presents a new statistical prediction methodology that is resolution consistent (i.e., preserves mass balance across resolutions) and computes spatial predictions and prediction (co)variances extremely fast. Data from the Total Ozone Mapping Spectrometer (TOMS) instrument, on the Nimbus-7 satellite, are used for illustration