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General
linear model. Properties and geometry of least-squares estimation.
General linear hypothesis, confidence regions and intervals.
Multicollinearity. Relationship between ANOVA models and linear models.
Residual analysis, outliers, partial and added variable plots.
This course is intended for students who need a solid knowledge of
regression analysis. The emphasis, in contrast to MATH3330.30, will be
a more
mathematical development of linear models including modern regression
techniques.
Students will use the computer for some exercises, but no previous
courses in computing are required. The statistical software package
SPLUS in a UNIX environment will be used and instructions will be
given in class.
The text is R.H. Myers, Classical and Modern Regression with
Applications (Duxbury).
The final grade will be based on a combination of assignments,
midterm tests and a final examination.
Prerequisite:For 1999/2000: AS/SC/MATH 1132 3.0, or an average
of B or higher in AS/SC/AK/MATH 2560 3.0 and AS/SC/AK/MATH 2570
3.0; AS/SC/MATH 2022 3.0 or AS/SC/AK/MATH 2222 3.0.
For
2000/01: AS/SC/AK/MATH 2131 3.0 or permission of the course
coordinator; AS/SC/MATH 2022 3.0 or AS/SC/AK/MATH 2222 3.0.
CorequisiteFor 1999/2000: AS/SC/AK/MATH 3131 3.0 or permission
of the course coordinator.
For 2000/01: No corequisite.
Exclusion:AS/SC/AK/MATH 3330 3.0, AS/SC/GEOG 3421
3.0, AS/SC/PSYC 3030 6.0.
Coordinator: P. Song
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