Fall 2017 Course Information
Applied Variance Component Estimation
||Colorado State University
||1 credit hours - $565 per credit
||October 30, 2017 - December 08, 2017
The goal of this course is to extend upon content covered in linear models and genetic prediction, with specific emphasis on estimation of (co)variance components and genetic parameters required to solve mixed models typical in livestock genetics. Upon successful completion of this course, students should have an applied knowledge of approaches used to estimate the G and R submatrices of the mixed model equations. Several tools will be used to demonstrate the models and approaches most commonly used in parameter estimation. Where appropriate, scientific literature that explains their implementation, and some attributes of the solutions obtained will be used. A general knowledge of linear models, matrix algebra, moment statistics, rules of expectation and familiarity with UNIX/Linux Operating Systems will be assumed, including scripting tools such as awk, octave, join, sort, paste, wc, etc. This course will begin in a somewhat historical manner, proceeding on to methods and software currently used for research and field data implementation.
For course access questions, contact the teaching university’s campus coordinator. For enrollment questions, contact your home university campus coordinator.
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Disability Support Services
To request accommodations for this course, contact the disability support office at your home university. You must register each semester and for each course. Read more about the Great Plains IDEA process for requesting accommodations.
Approximately three weeks before the first day of class at Colorado State University, the CSU campus coordinator, Gayle Roslund
, emails course access instructions to students for courses taught by CSU. Using these instructions, students create their Colorado State eID (electronic identity). Students meeting all deadlines for eID creation and submission will have access to RamCT by the first day of class.
This course does not require an exam proctor.
This course does not include synchronous components.