In modern terminology, the conditional mean E(Y|X=x) for a continuous distribution is called the regression function of Y on X and the graph of this function of x is known as the regression curve of Y on X or sometimes the regression curve for the mean of Y. Geometrically, the regression function represents the y co- ordinate of the centre of mass of the bivariate probability mass in the infinitesimal vertical strip bounded by x and x+dx.

Similarly, the regression function of X on Y is E(X|Y=y) and the graph of this function of y is called the regression curve of X on Y.

In case a regression curve is a straight line, the corresponding regression is said to be linear. If one of the regression is linear, it does not however follow that the other is also linear.

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- Bivariate Normal Distribution
- Multiple And Partial Correlation
- Plane Of regression
- Properties If residuals
- Coefficient Of Multiple Correlation
- Properties of Multiple Correlation coefficient
- Coefficient Of Partial Correlation
- Multiple correlation In terms Of Total And Partial Correlation
- Coefficient In Terms Of Regression
- Expression For Partial Correlation

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