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1. Introduction 2. Parametric regression 3. Scatterplot smoothing 4. Mixed models 5. Automatics scatterplot smoothing 6. Inference 7. Simple semiparametric models 8. Additive models 9. Semiparametric mixed models 10. Generalized parametric regression 11. Generalized additive models 12. Interaction Models 13. Bivariate smoothing 14. Variance function estimation 15. Measurement error 16. Bayesian semiparametric regression 17. Spatially adaptive splines 18. Analyses of case studies 19. Epilogue A. Matrix and linear algebra B. Vector differential equation C. Useful results from probability theory D. Theory for penalized splines E. Computational issues.