Revision #589 → #1369 · back to history
addedLinear regression6679e12d3963
addedLinear in parameters7ec4b0c66794
addedExamples of linear regressionaa56ab06c65e
addedExamples of non-linear regressionc74fc4a920df
addedSimple vs multiple linear regressioncbaa097550bf
addedLinear regression model449cd1ea13c9
addedRegressand / dependent variable9f6ac370f444
addedRegressors / design matrix146fa5e26a56
addedParameter vector / regression coefficients2ebe38ab3100
addedError term6ae34f2caed0
addedSum of squared errorsd6c47e3ac63d
addedBall tossed in the air532ef9987c50
addedUnique effect (interpretation of coefficient)8d8b89496b15
addedSimple linear regression4746ced760db
addedMultiple linear regression08f36dcbebd5
addedMultivariate linear regressione571e5eb1b6e
addedGeneral linear modelf976fa663bac
addedWeighted least squares (heteroscedastic)00caad333a64
addedGeneralized linear model4bb43d0702f7
addedLink function62eb8e2c69d1
addedHierarchical linear modelscaca4d725b30
addedErrors-in-variables modelse61e11e2ebfe
addedGroup effecta16289970e6b
addedMeaningful group effectd27882af3117
addedMinimum-variance unbiased estimator of group effect69cc66fb5413
addedDempster–Shafer swept-matrix representation63b46f540850
addedLeast-squares optimum parameter314078402892
addedConvex loss minimized at gradient zero0592a3d5699b
addedNormal equations (gradient zero solution)8e33e81285c8
addedGauss–Markov theorem75d8bd4af88c
addedMaximum likelihood estimationd527a0ef587f
addedNormal errors give OLS estimate23f668d7d628
addedLeast squares equals MLEdaa718247773
addedRegularized (ridge/lasso) regression45dbf4637f21
addedLeast absolute deviation regression0c49e40368d9
addedLAD equals MLE under Laplace model60d93810503e
addedAdaptive estimation optimal estimatorae5adb5b045e
addedBayesian linear regression5b316b73126f
addedQuantile regressionf9e5c70bf3b9
addedMixed models00b4b929bb42
addedPrincipal component regression9aac6c450b79
addedLeast-angle regression8e7357491ffc
addedTheil–Sen estimator1e907d17c71d
addedTrend lined59cd0a148f8