I'm interested in "evolution" of regression from SLR (Simple Linear Regression) to NPR (Non-Parametric Regression). Is there any good reference, which nicely explains this?
Suggested reading:
Non-Parametric Statistical Diagnosis: Problems and Methods (Mathematics and Its Applications)
X quantitative: Y quantitative = Pearson correlation
X quantitative: Y ordinal = Biserial correlation
X quantitative: Y nominal = Point biserial correlation
X ordinal: Y quantitative = Biserial correlation
X ordinal: Y ordinal = Spearman rho or tetrachoric correlation
X ordinal: Y nominal = Rank biserial correlation
X nominal: Y quantitative = Point biserial correlation
X nominal: Y ordinal = Rank biserial correlation
X nominal: Y nominal = Phi, L, C & Lambda
Suggested by (Utkarsh Goel)
Suggested reading:
Non-Parametric Statistical Diagnosis: Problems and Methods (Mathematics and Its Applications)
X quantitative: Y quantitative = Pearson correlation
X quantitative: Y ordinal = Biserial correlation
X quantitative: Y nominal = Point biserial correlation
X ordinal: Y quantitative = Biserial correlation
X ordinal: Y ordinal = Spearman rho or tetrachoric correlation
X ordinal: Y nominal = Rank biserial correlation
X nominal: Y quantitative = Point biserial correlation
X nominal: Y ordinal = Rank biserial correlation
X nominal: Y nominal = Phi, L, C & Lambda
Suggested by (Utkarsh Goel)
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