Science abounds with problems where the data are noisy and the answer is not a straight line. Semiparametric regression analysis helps make sense of such data in application areas that include engineering, finance, medicine and public health. The book is geared towards researchers and professionals with little background in regression as well as statistically oriented scientists (biostatisticians, econometricians, quantitative social scientists, and epidemiologists) with knowledge of regression and the desire to begin using more flexible semiparametric models.

1. Introduction; 2. Parametric regression; 3. Scatterplot smoothing; 4. Mixed models; 5. Automatic 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 smoothing; 18. Analyses; 19. Epilogue; A. Technical complements; B. Computational issues.