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Course Criteria
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1.00 - 6.00 Credits
No course description available.
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3.00 Credits
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2) [Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
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1.00 - 6.00 Credits
No course description available.
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1.00 - 6.00 Credits
No course description available.
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3.00 Credits
In this course, students complete an applied statistics field project that has been solicited from researchers in biological, physical, or social sciences. The instructor supplies applied as well as statistical methodology readings for the students. The readings serve to extend what students have learned in prior classes, and especially to help students learn to apply statistical methodology to problems from real-world applications. The students work in groups of 2, and they have to write a 10-20 page technical report and prepare a poster to summarize the project. Satisfies the Integrative Experience requirement for BA-Math and BS-Math majors. Prerequisites: Previous coursework in Probability and Statistics, such as Statistc 516 or 501, as well as Statistc 505 and 506 including knowledge of estimation, intervals, and hypothesis testing, or permission of instructor.
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1.00 - 6.00 Credits
No course description available.
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1.00 - 18.00 Credits
University of Massachusetts Amherst has not provided a description for this course
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3.00 Credits
For graduate and upper-level undergraduate students, with focus on practical aspects of statistical methods.Topics include: data description and display, probability, random variables, random sampling, estimation and hypothesis testing, one and two sample problems, analysis of variance, simple and multiple linear regression, contingency tables. Includes data analysis using a computer package. Prerequisites: high school algebra; junior standing or higher. [Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
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3.00 Credits
Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection; expressing regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear regression. Extensive data analysis using R or SAS (no previous computer experience assumed). Requires prior coursework in Statistics, preferably ST516, and basic matrix algebra. Satisfies the Integrative Experience requirement for BA-Math and BS-Math majors.
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3.00 Credits
Planning, statistical analysis and interpretation of experiments. Designs considered include factorial designs, randomized blocks, latin squares, incomplete balanced blocks, nested and crossover designs, mixed models. Has a strong applied component involving the use of a statistical package for data analysis. Prerequisite: previous coursework in statistics.
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