1. Fitting a straight line by least squares -- 2. The matrix approach to linear regression -- 3. The examination of residuals -- 4. Two predictor variables -- 5. More complicated models -- 6. Selecting the "best" regression equation -- 7. Two specific problems -- 8. Multiple regression and mathematical model building -- 9. Multiple regression applied to analysis of variance problems -- 10. An introduction to nonlinear estimation.
