Linear Regression Analysis
In this field the basic methods to carry out regression analysis are studied. The emphasis is on data that are characterised by many variables. The focus is on finding good latent structure that generates the response variables. The latent structure is generated in a way that optimises the prediction aspect of the model. Extensive graphic procedures illustrate the inherent variation of data.
Topics
| 1. | Introduction. Ideas of the H-method |
| 2. | Latent structures |
| 3. | Importance of covariance. The H-principle |
| 4. | Linear regression analysis |
| 5. | Plots using score vectors |
| 6. | Plots showing loading vectors |
| 7. | Plots of measures of regression analysis |
| 8. | Cross-validation |
| 9. | The H-method |
| 10. | Quantitative measures for variables |
| 11. | Selection/ordering of variables |
| 12. | Subset selection of variables |
| 13. | Dimension analysis |
| 14. | Analysis of residuals |
| 15. | Outlier detection |
| 16. | Sensitivity analysis |
| 17. | Confidence intervals |
| 18. | Detection of special features |
| 19. | Loading weight vectors |
| 20. | Useful measures in linear regression |
| 21. | Guidelines for presentation of results |
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