Multiple linear regression model including parameter estimation, simultaneous confidence intervals and general linear hypothesis testing using matrix algebra. Applications to forecasting. Residual analysis and outliers. Model selection: best regression, stepwise regression algorithms. Transformation of variables and non-linear regression. Computer analysis of practical real world data.
This course may not be repeated for credit.
Notes
- Statistics 421[STAT421] is highly recommended as preparation
Prerequisite(s)
- Mathematics 323[MATH323] and 353.
SyllabusSections
| LEC 1 | TR 15:30 - 16:45
| | | | Outline |
| TUT 1 | W 16:00 - 16:50
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| TUT 2 | R 10:00 - 10:50
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This course will be offered next in
Fall 2010.