Template:Least squares and regression analysis From Wikipedia the free encyclopedia vteLeast squares and regression analysisComputational statistics Least squares Linear least squares Non-linear least squares Iteratively reweighted least squares Correlation and dependence Pearson product-moment correlation Rank correlation (Spearman's rho Kendall's tau) Partial correlation Confounding variable Regression analysis Ordinary least squares Partial least squares Total least squares Ridge regression Regression as a statistical modelLinear regression Simple linear regression Ordinary least squares Generalized least squares Weighted least squares General linear model Predictor structure Polynomial regression Growth curve (statistics) Segmented regression Local regression Non-standard Nonlinear regression Nonparametric Semiparametric Robust Quantile Isotonic Non-normal errors Generalized linear model Binomial Poisson Logistic Decomposition of variance Analysis of variance Analysis of covariance Multivariate AOV Model exploration Stepwise regression Model selection Mallows's Cp AIC BIC Model specification Regression validation Background Mean and predicted response Gauss–Markov theorem Errors and residuals Goodness of fit Studentized residual Minimum mean-square error Frisch–Waugh–Lovell theorem Design of experiments Response surface methodology Optimal design Bayesian design Numerical approximation Numerical analysis Approximation theory Numerical integration Gaussian quadrature Orthogonal polynomials Chebyshev polynomials Chebyshev nodes Applications Curve fitting Calibration curve Numerical smoothing and differentiation System identification Moving least squares Regression analysis category Statistics category Mathematics portal Statistics outline Statistics topics