Multiple linear regression model, parameter estimation, simultaneous confidence intervals and general linear hypothesis testing. Residual analysis and outliers. Model selection: best regression, stepwise regression algorithms. Transformation of variables and non-linear regression. Applications to forecasting. Variable selection in high-dimensional data using linear regression. Computer analysis of practical real world data.
This course may not be repeated for credit.
Prerequisite(s)
- Statistics 323 or Data Science 305; and Mathematics 211 or 213.
Sections
| LEC 1 | TR 14:00 - 15:15
| | Jingjing Wu | | Outline |
| TUT 1 | R 09:00 - 09:50
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| TUT 2 | R 10:00 - 10:50
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| TUT 3 | R 09:00 - 09:50
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This course will be offered next in
Fall 2022.