Applied Linear Statistical Models I
This course on Applied Linear Statistical Models is a post-graduate and doctoral level course.
One of the primal aims of the course will be to develop and analyze Linear Statistical Models in
the areas of Regression Analysis, Analysis of Variance, and Experimental Design. These linear
models are used in a wide variety of problem-situations and applications in the real world
within the domains of physical/natural and social sciences like elementary particle physics,
engineering, molecular biology and bio-physics, business administration, economics, social,
health and biological sciences. The course will try to provide the optimal mix of theory and
applications of linear statistical models so that the students will have a sound understanding of
both the underlying theory and its applications. The material covered in this course will enable
the students to do further advanced statistical courses like Applied Linear Statistical Model II
based on the theory and applications of Multivariate Statistical Models and Techniques like
Multivariate Multiple Regression, Principal Component Analysis, Factor and Discriminant
Analysis, Conjoint Analysis, and Multi-Dimensional Scaling.
The students in this course will also become familiar with a number of very nice and efficient
statistical software packages like SPSS, Stata,etc. The Topics marked with * will be covered, if
time permits.
Topics:
I)
II)
III)
IV)
V)
VI)
VII)
Review of Basic Probability and Statistics
Simple Linear Regression: Inferences, Diagnostics & Remedial Measures,
Simultaneous Inferences & Other Topics in Regression Analysis
Matrix Approach to Simple Linear Regression
General Linear Regression: Multiple & Polynomial Regression, Diagnostics &
Remedial Measures, Qualitative Independent Variables, Building the Regression
Model, Autocorrelation in Time Series.
Single-Factor Analysis of Variance: Single-Factor ANOVA Model, Analysis of Factor
Level Effects, Diagnostics & Remedial Measures.
Multifactor Analysis of Variance*: Two-Factor Analysis of Variance, Random and
Mixed Effects Models, ANCOVA
Experimental Designs*:Randomized Blocks, Nested Designs and Subsampling,
Repeated Measures & Related designs, Latin Squares & Related Designs
Course Designer & Instructor: Ahmad Raza
Grading Plan:
Course File & Viva: 10%
Quizzes & Assignments: 10%
Term Exams: 40%
Final Exam: 40%
Reference & Articles Books:
Neter, John, William Wasserman,Michael H.Kutner, Applied Linear Statistical Models,
Publishers: McGraw-Hill Irwin 2005
Graybill, Frank A., Theory & Applications of Linear Models, Publishers: Duxbury Press 2000.
Stapleton, James H., Linear Statistical Models, Publishers: Wiley Series in Probability &
Statistics, 2008.
Johnson, Richard A., and Dean W Wichern, Applied Multivariate Statistics, Publishers:
Prentice-Hall 1988.
Simo, Putanen, Styman George, Isatalo Jarkko, Matrix Tricks for Linear Models, Publishers:
Springer Verlag 2011.
Draper, R Norman, Harry Smith, Applied Regression Analysis, Wiley Series in Probability and
Statistics, Publishers: John Wiley 1981.
Myers, Raymond H, Classical & Modern Regression with Applications, Publishers: Duxbury
Press, 1986.
Durbin, J., and G.S.Watson, Testing for Serial Correlation in Least Squares Regression.II,
Biometrika 38(1951) pp.159-78.