Instructor: Paul J. Smith, Statistics Program
Office hours: MWF 1-2, MTH 1107
Textbooks:
Prerequisites: STAT 420 or STAT 700. You will be expected to be proficient with topics in matrix and linear algebra, including orthogonal projection, eigenvalues and eigenvectors.
The goal of applied statistics is to model relationships between variables and to perform inferences on real world situations based on statistical models. Regression analysis and analysis of variance are common techniques to model quantitative response variables; these are examples of linear statistical models, in which a response variable is modeled as a linear function of predictors plus a random error term. Linear models are analyzed using least squares, nowadays using statistical software packages like SAS or R/S-Plus.
STAT 740-741 is a year long sequence dealing with linear models and some of their extensions. The material covered in these courses is central to applied statistical methodology and is also part of the Graduate Written Examination in Applied Statistics. The first semester deals mainly with least squares, regression analysis and basic analysis of variance models. The second semester deals with more complex analysis of variance models, random and mixed effects models, and generalized linear models for discrete response variables.
These courses are primarily applied, although theoretical topics will be treated as necessary. Data analysis and interpretation are an essential component of the course, and students will analyze real world data sets, principally using the R statistical computing package (although students may choose to use other packages instead).
STAT 740 Topics:
References
Agresti, A. (2015). Foundations of Linear and Generalized Linear Models Hoboken, NJ: J. Wiley.Cody, R. P. and Smith, J. K. (2006). Applied Statistics and the SAS Programming Language (5th ed.) Upper Saddle River, NJ: Prentice-Hall.
Christensen, R. (2002). Plane Answers to Complex Questions: The Theory of Linear Models. (3rd ed.) New York: Springer.
Daniel, C. and Wood, F. S. (1980). Fitting Equations to Data (2nd ed.) New York: J. Wiley.
Draper, N. R. and Smith, H. (1998). Applied Regression Analysis (3rd ed.) New York: J. Wiley.
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.
McCulloch, C. E., Searle, S. R. and Neuhaus, J. M. (2008). Generalized, Linear and Mixed Models. (2nd ed.) New York: Wiley.
Milliken, G. and Johnson, D. (2009). Analysis of Messy Data, Vol. I: Designed Experiments. (2nd ed.) New York: CRC Press.
Monahan, J. F. (2008). A Primer on Linear Models. CRC/Chapman & Hall.
Rencher, A. C. & Schaalje, G. B. (2008). Linear Models in Statistics. (2nd ed.) New York: J. Wiley.
Scheffe, H. (2008) The Analysis of Variance. Hoboken, NJ: J. Wiley.
Searle, S. R., Casella, G. and McCulloch, C. E. (1992). Variance Components. New York: J. Wiley.
Stapleton, J. H. (2009). Linear Statistical Models. (2nd ed.) New York: J. Wiley.
Venables, W. N. & Ripley, B. D. (2002). Modern Applied Statistics with S. (4th ed.) New York: Springer.
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