Syllabus


 
Instructor Dr. Xuerong Meggie Wen
Email:  wenx@mst.edu
Office: 113 Rolla Bldg
Phone: 341-6209
Office Hours 1:00--2:00 T Th , or by appointment
 
Lectures 11:00-12:15 T TH, 207 Computer Science
 
Text Linear Models in Statistics(2nd edition), by Alvin C. Rencher and G. Bruce Schaalje.
Generalized, Linear, and Mixed Models (2nd edition), by McCulloch, Shayle Searle and John Neuhaus
Objective

This course is concerned with the theory and application of linear models. The first part of the course will focus on general linear model theory from a coordinate-free geometric approach. The second half of the course covers theory, applications and computing for linear models, and concentrates on modeling, computation and data analysis.

 
Attendance Students missed 4 or more classes on Jan. 31; 6 or more classes for the semester with no reasonable explanation will be dropped from the course. Distance session students who miss to email the instructor a password given in each lecture before the next lecture will be counted as absent.
Class Web Page www.mst.edu/~wenx/teaching/stat6554
Homework and Quizzes

There will be problem sets assigned every (other) week for homework. Homework will be collected and solutions to some assigned problems will be posted on the website. Homework will be graded and the lowest grade will be dropped. For fairness and clarity we have the following rules about homework.

  1. Make-up homework will not be accepted, unless prior permission has been obtained from the instructor.

  2. Copying of someone else's work is not acceptable, nor is misrepresenting the work of others as your own. 

  3. Homework solutions submitted for grading should be legible and neat. Use sentences and explain your work. In grading quizzes, both the correctness of the answer and organization of ideas and presentation count. A correct numerical answer with no explanation will receive little credit.

Exams

There will be one project and a final take-home exam. All work on exams must be entirely your own. Any dishonesty during exams will lead to an automatic grade of zero for the exam. It is just as dishonest to give help as to receive it. Missed exams will be graded as zero unless you can convince me that there was a very good reason for the missed exam. I might require written documentation. Details on class project will be provided later this semester.
Grading

Your final grade will be based on homework (20%),  project(30%), and take-home exam (50%). Grade will roughly follow that, 90-100 = A, 80 - 90 = B, 70 - 80 = C, 60 - 70 = D, and 0 - 60 = F.  If you registered S/N a grade of C or higher is needed to earn an S. 
 
Lectures: I will follow the textbook reasonably close. Methods, examples and applications outside the book will be presented in class, so you are encouraged to attend all the lectures. You can (and are encouraged to) ask questions at any time.

 

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