Least squares

• Statistical methods

  • Regression analysis
  • Penalized least squares

• Mathematical optimization

  • Linear least squares
  • Nonlinear least squares
  • Moving least squares
  • Regression analysis

• Applied mathematics

  • Total least squares
  • Numerical linear algebra
  • Surface fitting
  • Nonlinear least squares
  • Moving least squares

• Regression analysis (119)

  • Coefficient of determination
  • Dependent variable
  • Errors in variables
  • Feasible generalized least squares
  • Generalized least squares
  • Generalized least squares
  • Generalized linear models
  • Isotonic regression
  • Iteratively reweighted least squares
  • Least squares regression
  • Linear model
  • Local regression
  • Multicollinearity
  • Non-linear regression
  • Ordinary least squares
  • Partial least squares
  • Polynomial regression
  • Principal component regression
  • Robust regression
  • Segmented regression
  • Simple linear regression
  • Sum of squares
  • Three-stage least squares
  • Two stage least squares
  • Autocorrelation
  • Bayesian linear regression
  • Binomial regression
  • Breusch–Pagan test
  • CHAID
  • Calibration (statistics)
  • Canonical analysis
  • Censored regression model
  • Comparison of general and generalized linear models
  • Conditional change model
  • Cook's distance
  • Cross-sectional regression
  • DFFITS
  • Deming regression
  • Design matrix
  • Difference in differences
  • Dummy variable (statistics)
  • Explained sum of squares
  • Explained variation
  • First-hitting-time model
  • Fraction of variance unexplained
  • Generalized additive model
  • Generalized linear array model
  • Generalized linear mixed model
  • Guess value
  • Hat matrix
  • Heckman correction
  • Heterosced- asticity-consistent standard errors
  • Indirect least squares
  • Interaction variable
  • Kitchen sink regression
  • Lack-of-fit sum of squares
  • Least absolute deviations
  • Leverage (statistics)
  • Linear least squares
  • Linear probability model
  • Logistic regression
  • Mallows' Cp
  • Mean and predicted response
  • Mixed model
  • Moderation (statistics)
  • Moving least squares
  • Multinomial logit
  • Multiple correlation
  • Multivariate adaptive regression splines
  • Multivariate probit
  • Newey–West estimator
  • Nonlinear least squares
  • Nonparametric regression
  • Numerical smoothing and differentiation
  • Omitted-variable bias
  • Optimal design
  • Ordered logit
  • Outline of regression analysis
  • Overfitting
  • Partial leverage
  • Partial regression plot
  • Partial residual plot
  • Path analysis (statistics)
  • Path coefficient
  • Penalized least squares
  • Poisson regression
  • Polynomial and rational function modeling
  • Prediction interval
  • Probit model
  • Propensity score matching
  • Proper linear model
  • Proportional hazards models
  • Quantile regression
  • Ramsey RESET test
  • Random multinomial logit
  • Regression Analysis of Time Series
  • Regression dilution
  • Regression discontinuity
  • Regression toward the mean
  • Regression variable selection
  • Residual sum of squares
  • Savitzky–Golay smoothing filter
  • Seemingly unrelated regression
  • Semiparametric regression
  • Sinusoidal model
  • Smearing retransformation
  • Specification (regression)
  • Standardized coefficient
  • Statistical outliers
  • Stepwise regression
  • Tobit model
  • Total least squares
  • Total sum of squares
  • Trend analysis
  • Truncated regression model
  • Unit-weighted regression
  • Variable rules analysis
  • Variance inflation factor
  • White test
  • gretl
  • more...

• Parametric statistics (12)

  • Analysis of variance
  • D'Agostino's K-squared test
  • Fraction of variance unexplained
  • Group family
  • Least squares regression
  • Least squares regression
  • Location-scale family
  • Normality test
  • Ordinary least squares
  • Pearson product-moment correlation coefficient
  • Simple linear regression
  • Student's t-statistic
  • Student's t-test
  • more...

• Statistical models

  • Variance components
  • Regression analysis
  • Total least squares
  • Errors in variables
  • Linear model
  • Segmented regression
  • Generalized linear models

• Estimation theory

  • Maximum likelihood
  • M-estimator
  • Kalman filter
  • Least squares regression
  • Regression analysis
  • Ordinary least squares
  • Simple linear regression

• Data analysis

  • Covariance matrix
  • Principal component analysis
  • Regression analysis
  • Analysis of variance
  • Segmented regression

• Econometrics

  • Heteroskedasticity
  • Structural equation models
  • Regression analysis
  • Two stage least squares
  • Three-stage least squares
  • Errors in variables

• Statistics (9)

  • Non-parametric estimation
  • Probability distributions
  • Regression analysis
  • Three-stage least squares
  • Penalized least squares
  • Penalized least squares
  • Feasible generalized least squares
  • Sum of squares
  • Linear model
  • Goodness of fit
  • more...

• Singular value decomposition (17)

  • Eigenvalues and eigenvectors
  • Singular value
  • Blind signal separation
  • Ervand Kogbetliantz
  • Gene H. Golub
  • Gene H. Golub
  • Generalized singular value decomposition
  • Kernel (matrix)
  • Linear least squares
  • Moore–Penrose pseudoinverse
  • Normal mode
  • Orthogonal Procrustes problem
  • Principal component analysis
  • Schmidt decomposition
  • Singular value decomposition (Ky Fan norms)
  • Singular value decomposition (Matrix approximation)
  • Spectral theorem
  • Two-dimensional singular value decomposition
  • more...

• Linear algebra (9)

  • Simultaneous linear equations
  • Over determined
  • Matrix theory
  • Rank deficient
  • Toeplitz matrix
  • Toeplitz matrix
  • Linear least squares
  • Eigenvalues and eigenvectors
  • Singular value decomposition
  • Numerical linear algebra
  • more...

• Numerical analysis (15)

  • Approximation theory
  • Chebyshev nodes
  • Chebyshev polynomials
  • Gaussian quadrature
  • Numerical analysis
  • Numerical analysis
  • Numerical integration
  • Orthogonal polynomials
  • Polynomial curve
  • Data fitting
  • Numerical differentiation
  • Finite differences
  • Numerical stability
  • Nonlinear least squares
  • Isotonic regression
  • Numerical linear algebra
  • more...

• Equations

  • Parametric equations
  • Linear equality
  • Lorentz transformation
  • Simultaneous linear equations

• Matrices

  • Tridiagonal
  • Symmetric positive definite
  • Symmetric matrix
  • Pseudoinverse
  • Eigenvalues and eigenvectors
  • Covariance matrix
  • Toeplitz matrix

• Optimization algorithms

  • Levenberg-Marquardt
  • Quasi-newton methods
  • Simplex algorithm
  • Gauss-Newton

• Matrix decompositions (13)

  • Lu decomposition
  • QR decomposition
  • Block LU decomposition
  • Cholesky decomposition
  • Crout matrix decomposition
  • Crout matrix decomposition
  • Eigendecomposition of a matrix
  • Jordan normal form
  • Jordan–Chevalley decomposition
  • Matrix decomposition
  • Polar decomposition
  • Schur decomposition
  • Singular value decomposition
  • Symbolic Cholesky decomposition
  • more...

• Least squares methods (8)

  • Weighted least squares
  • Linear least squares
  • Partial least squares regression
  • Total least squares
  • Non-linear least squares
  • Non-linear least squares
  • Ordinary least squares
  • Generalized least squares
  • Iteratively reweighted least squares
  • more...

• See also (20)

  • Recursive least squares
  • Least mean squares
  • B splines
  • Cholesky
  • Procrustes analysis
  • Procrustes analysis
  • Linear systems
  • Cubic spline
  • Theory of errors
  • Kernel estimation
  • Bayesian experimental design
  • Best linear unbiased prediction
  • Calibration curve
  • L2 norm
  • Measurement uncertainty
  • Minimum mean square error
  • Model selection
  • Response surface methodology
  • Root mean square
  • Squared deviations
  • Tikhonov regularization
  • more...

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