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Institution:
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Duke University
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Subject:
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Environment
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Description:
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Graphical and exploratory data analysis; modeling, estimation, and hypothesis testing; analysis of variance; random effect models; nested models; regression and scatterplot smoothing; resampling and randomization methods. Concepts and tools involved in data analysis. Special emphasis on examples drawn from the biological and environmental sciences. Students to be involved in applied work through statistical computing using software, often S-plus, which will highlight the usefulness of exploratory methods of data analysis. Other software, such as SAS, may be introduced. Instructor: Qian
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Credits:
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3.00
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Credit Hours:
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Prerequisites:
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Corequisites:
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Exclusions:
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Level:
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Instructional Type:
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Lecture
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Notes:
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Additional Information:
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Historical Version(s):
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Institution Website:
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Phone Number:
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(919) 684-8111
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Regional Accreditation:
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Southern Association of Colleges and Schools
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Calendar System:
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Semester
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