Category:Regression analysis

M

• Regression models (5 C, 46 P)

N

• Nonparametric regression (9 P)

R

• Regression and curve fitting software (22 P)
• Regression diagnostics (25 P)
• Regression variable selection (16 P)
• Regression with time series structure (2 C, 17 P)
• Robust regression (9 P)

S

• Simultaneous equation methods (econometrics) (1 C, 5 P)
• Single-equation methods (econometrics) (3 C, 14 P)

• Regression analysis
• Outline of regression analysis

A

• Abess
• Antecedent variable

B

• Bazemore v. Friday
• Binary regression

C

• C+-probability
• Calibration (statistics)
• Canonical analysis
• Causal inference
• Commonality analysis
• Component analysis (statistics)
• Conjoint analysis
• Contrast (statistics)
• Cross-sectional data
• Cross-sectional regression

D

• DeFries–Fulker regression
• Deming regression
• Dependent and independent variables
• Design matrix
• Difference in differences

E

• Elastic net regularization
• Endogeneity with an exponential regression function
• Errors and residuals
• Explained variation

F

• Fractional model
• Frisch–Waugh–Lovell theorem
• Function approximation
• Functional regression

G

• G-prior
• Gelbach decomposition
• General regression neural network
• Generalized estimating equation
• Generated regressor
• Guess value

H

• Haseman–Elston regression
• Heckman correction
• Heteroskedasticity-consistent standard errors
• Homoscedasticity and heteroscedasticity

I

• Idempotent matrix
• Identifiability analysis
• Instrumental variables
• Interaction (statistics)
• Interaction cost
• Interval predictor model

K

• Knockoffs (statistics)

L

• Lasso (statistics)
• Least-squares support vector machine
• Leave-one-out cross-validation
• Limited dependent variable
• Line fitting
• Linear predictor function
• Linkage disequilibrium score regression

M

• Meta-regression
• Moderated mediation
• Moderation (statistics)
• Multicollinearity
• Multinomial probit
• Coefficient of multiple correlation

N

• Non-linear mixed-effects modeling software
• Nonhomogeneous Gaussian regression
• Nonlinear regression

O

• Kitagawa–Oaxaca–Blinder decomposition
• Omitted-variable bias
• Optimal experimental design

P

• Policy capturing
• Polygenic score
• Polynomial regression
• Prediction interval
• Principal component regression
• Principle of marginality
• Projection matrix
• Projection pursuit regression
• Propensity score matching
• Pyrrho's lemma

Q

• Quantile regression
• Quantile regression averaging

R

• Radial basis function network
• Regression discontinuity design
• Regression toward the mean
• Ridge regression

S

• Scatterplot smoothing
• Simalto
• Simple linear regression
• Sliced inverse regression
• Smearing retransformation
• Smoothing spline
• Sobel test
• Standardized coefficient
• Structural break
• Suppressor variable
• Symbolic regression

T

• Target function

U

• Under-fitting
• Underfitting
• Unit-weighted regression

V

• Variance of the mean and predicted responses
• Virtual sensing

W

• Working–Hotelling procedure