Required Textbooks: 

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

  • Faraway, J. J. Linear Models in R. (2nd ed., 2015) Boca Raton, FL: Chapman & Hall/CRC.

  • Faraway, J. J. Extending the Linear Model with R. (2nd ed., 2016) Boca Raton, FL: Chapman & Hall/CRC.

    Recommended Textbooks: 

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

    Instructor: Paul J. Smith, Statistics Program

    Schedule:  Spring 2021, MWF 2, using Zoom.

    Prerequisites:  STAT 740 or consent of instructor.

    Teaching format:  Because of the COVID 19 pandemic, STAT 740 will be taught on line using ELMS and Zoom. We will meet virtually during the scheduled class time, I will upload class notes to ELMS and the sessions will be recorded on ELMS. During the scheduled class time, we will have two way video contact and live interaction. If you are unfamiliar with the technology, see the Keep Learning website here.

    Course Description: 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.


    Exams and Grading:

    Midterm: Friday, March 12. using Zoom. (Click here for practice problems).

    Final: Monday, May 17, 1:30-3:30 p.m., using Zoom.

    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. Homework assignments will be posted on ELMS.

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


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

    Clarke, B. R. (2008), Linear Models.  New York: J. Wiley.

    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.

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