PrefaceNotationI IntroductionII Linear Lp Regession 2.1 Fundamentals 2.2 ρ = 2 (Method of the Least Squares: NGL, MGS, ICMGS, GIVR, HFTI, SVDR) 2.3 ρ ≠ 1,2, ∞ (LPREGR) 2.4 ρ = 1 (Method of Least Absolute Deviations: A478L1, AFKL1, BLOD1) 2.5 ρ = ∞ (Method of Least Maximum Absolute Deviation: A328LI, A495LI, ABDLI) 2.6 Comparison of Residuals (RES) and Choice of p 2.7 The Elimination of Outliers 2.8 Selection of Variables (SCR, SCRFL1) 2.9 Clusterwise Linear Regression (CWLL2R, CWLL1R, CWLLIR) 2.10 Average Linear Regression (AVLLSQ)III Robust Regression (ROBUST) 193IV Ridge Regression (RRL2, RRL1, RRL1)V Linear Lp Regression with Linear Constraints 5.1 Introduction 5.2 p = 2(CL2) 5.3 p = 1 (CL1) 5.4 ρ = ∞ (CLI)VI Linear Lp Regression with Nonnegative Parameters (p = 2: NNLS; p = 1: NNL1; p = ∞: NNL1)VII Orthogonal Linear Lp Regression 7.1 Fundamentals 7.2 p = 2(L2ORTH) 7.3 p ≠ 1,2, ∞ (LPORTH) 7.4 p = 1 (L1ORTH) 7.5 p = ∞ (L1ORTH) 7.6 Comparison of Residuals and Choice of p 7.7 Orthogonal L2 Regression with Linear Manifolds (LMORTH)Final RemarksList of SubroutinesAppendix: ExamplesIndex