STAT 741:  LINEAR MODELS II

COURSE OUTLINE, SPRING 2018


Required Textbooks: 

  • Clarke, B. R. Linear Models.  New York: J. Wiley.

  • Faraway, J. J. Extending the Linear Model with R. (2nd ed.) Boca Raton, FL: Chapman & Hall/CRC.


    Recommended Textbooks: 

  • Faraway, J. J. Linear Models in R. (2nd ed.) Boca Raton, FL: Chapman & Hall/CRC.

  • Scheffe, H. (1958).  The Analysis of Variance.  New York: J. Wiley.

    Instructor: Paul J. Smith, Statistics Program

    Schedule:  Spring 2018, MWF 2, MTH 0102

    Prerequisites:  STAT 740 or consent of instructor.

    STAT 741 is the second semester of a year-long sequence STAT 740-741 dealing with analysis of linear models, least squares and related topics.  This course deals with complex analysis of variance models, random and mixed effects models, and generalized linear models for discrete response variables.  Material from STAT 740-741 is part of the Graduate Written Examination in Applied Statistics.

    This course will deal with both applied and theoretical topics. Data analysis and interpretation are essential components of the course, and students will analyze real world data sets using the R statistical computing package.

    Topics:

    Exams and Grading:

    Midterm: Friday, March 15 (click here for practice problems).

    Final: Monday, May 20, 1:30-3:30 p.m. in MTH 0103.

    Homework: Frequent problem sets will be assigned. These will be a mix of theoretical and applied problems involving analysis of real data sets on the computer. Click here for homework assignments.

    Grading: The midterm and final will each count for approximately 20% of the grade and the homework will count for approximately 60%.

    References:

    Agresti, A. (2015), Foundations of Linear and Generalized Linear Models. New York: Springer.

    Christensen, R. (2002), Plane Answers to Complex Questions: The Theory of Linear Models (3rd ed.). New York: Springer.

    Cody, R. P. and Smith, J. K. (1997).  Applied Statistics and the SAS Programming Language.  Upper Saddle  River, NJ: Prentice-Hall.

    Hocking, R. (1996).   Methods and Applications of Linear Models.  New York: J. Wiley.

    McCullagh, P. and Nelder, J. A. (1989).   Generalized Linear Models (2nd ed).  New York: Chapman and Hall.

    Milliken, G. and Johnson, D. (1984).  Analysis of Messy Data, Vol. I: Designed Experiments.  New York: Van Nostrand-Reinhold.

    Monahan, J. F. (2008). A Primer on Linear Models. Boca Raton, FL: Chapman & Hall/CRC.

    Rao, P. S. R. S. (1997).  Variance Components Estimation.   New York: Chapman & Hall.

    Rencher, A. C. and Schaalje, G. B. (2008).  Linear Models in Statistics (2nd ed.). New York: J. Wiley.

    Searle, S. R., Casella, G. and McCulloch, C. E. (1992).   Variance Components.  New York: J. Wiley.

    Stapleton, J. (2009). Linear Statistical Models.  (2nd ed.)  New York: J. Wiley.