This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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477
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Education
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| Abstract - #308613 |
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Title:
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Enhancing the Teaching of Statistics: Analysis of Spatial Data Using SOCR and R
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Author(s):
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Nicolas Christou*+ and Ivo Dinov
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Companies:
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University of California, Los Angeles and University of California, Los Angeles
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Address:
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8125 Math Sciences Bldg., Los Angeles, CA, 90095,
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Keywords:
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SOCR ;
spatial ;
geostatistics ;
maps ;
environmental data ;
R
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Abstract:
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In this paper we discuss the exploration of the analysis of spatial data in introductory and other statistics courses. With the term spatial data we refer to data that are taken at geographic locations defined by a coordinate system (e.g. longitude and latitude). Environmental data are spatial data and can be found in areas such as air pollution, weather, water quality, economy, census, agriculture, ecology, mining. The analysis of spatial data is taught usually in advanced courses at the graduate level. Many theories for spatial data were developed in mining engineering where the term geostatistics was used. Using the new Statistics Online Computational Resource (SOCR) applet and R packages we show that the analysis of spatial data can be performed in a way that captures students' attention and interest. Examples of environmental spatial data are provided to support this hypothesis.
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