About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 150. Chapters: Linear regression, Autocorrelation, Least squares, Linear model, Overfitting, Ordinary least squares, Optimal design, Linear least squares, Errors-in-variables models, Regression toward the mean, Non-linear least squares, Generalized linear model, Interaction, Prediction interval, Instrumental variable, Multivariate adaptive regression splines, Structural equation modeling, Coefficient of determination, Total least squares, Robust regression, Least squares support vector machine, Sliced inverse regression, Least absolute deviations, Multicollinearity, Logistic regression, Generalized additive model for location, scale and shape, Simple linear regression, Proportional hazards models, Polynomial and rational function modeling, Polynomial regression, Sufficient dimension reduction, Local regression, Quantile regression, Partial least squares regression, Curve fitting, Fixed effects model, Seemingly unrelated regressions, Dependent and independent variables, Proofs involving ordinary least squares, Growth curve, Poisson regression, Difference in differences, Regression dilution, Errors and residuals in statistics, Stepwise regression, Numerical smoothing and differentiation, Unit-weighted regression, Backfitting algorithm, Lack-of-fit sum of squares, Heckman correction, Segmented regression, Nonlinear regression, Semiparametric regression, Explained sum of squares, Smoothing spline, Bayesian linear regression, Projection pursuit regression, Tobit model, Propensity score matching, Multinomial logit, Probit model, Explained variation, Generalized least squares, Mixed model, Deming regression, Path analysis, Calibration, Regression model validation, Binomial regression, Hat matrix, Bayesian multivariate linear regression, Mallows' Cp, Generalized estimating equation, Omitted-variable bias, CHAID, Isotonic regression, Mean...
