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Simple regression
analysis, multiple regression analysis, matrix form of the
multiple regression model, estimation, tests (t- and F-tests),
multicollinearity and other problems encountered in regression, diagnostics,
model building and variable selection, remedies for violations of regression
assumptions. First term.
This course is closely linked with MATH3034 3.0, Applied Categorical
Data Analysis, for which it is a prerequisite. Students will use the
computer heavily in these courses, but no previous courses in
computing are required.
MATH3330 3.0 will focus on linear regression models for the analysis
of data on several explanatory variables and a single response. The
emphasis
will be on understanding the different models and statistical concepts
used for these models and on practical applications, rather than on the
formal derivations of the models. The approach will require the use
of
matrix representations of the data, and the geometry of vector spaces,
which will be reviewed in the course. Topics include simple linear
regression, multiple linear regression, residual analysis and model
selection.
The nature of the course requires that students be involved on a
constant
basis with the material, and not fall behind.
The text and grading scheme have not been determined as we go to
press.
Prerequisite:One of AS/SC/MATH 1132 3.0,
AS/SC/AK/MATH 2131 3.0,
AS/SC/AK/MATH 2570 3.0, AS/SC/PSYC 2020 6.0,
or equivalent; some acquaintance with matrix algebra
(such as is provided in AS/SC/MATH 1025 3.0,
AS/SC/MATH 1505 6.0, AS/AK/MATH 1550 6.0,
AS/SC/MATH 2021 3.0, or AS/SC/AK/MATH 2221 3.0).
ExclusionsAS/\-SC/\-MATH 3033 3.0,
AS/SC/GEOG 3421 3.0, AS/SC/PSYC 3030 6.0,
AS/ECON 4210 3.0, AK/PSYC 3110 3.0.
Coordinator: Fall: P. Song Winter: D. Montgomery .
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