Regression and Correlation


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Regression is a way of describing how one variable, the outcome, is numerically related to predictor variables. The dependent variable is also referred to as Y, dependent or response and is plotted on the vertical axis (ordinate) of a graph. The predictor variable(s) is(are) also referred to as X, independent, prognostic or explanatory variables. The horizontal axis (abscissa) of a graph is used for plotting X.

Looking at a plot of the data is an essential first step. The graph above suggests that lower birth weight babies grow faster from 70 to 100 than higher birth weight babies. Linear regression can be used to fit a straight line to these data:


Equation: Y = a + bx


The fitted equation describes the best linear relationship between the population values of X and Y that can be found using this method.

The method used to fit the regression equation is called least squares. This minimises the sum of the squares of the errors associated with each Y point by differentiation. This error is the difference between the observed Y point and the Y point predicted by the regression equation. In linear regression this error is also the error term of the Y distribution, the residual error.


The simple linear regression equation can be generalised to take account of k predictors:


Y = b0 + b1x1 + b2x2 +...+ bkxk


Assumptions of general linear regression:





Correlation refers to the interdependence or co-relationship of variables.


In the context of regression examples, correlation reflects the closeness of the linear relationship between x and Y. Pearson's product moment correlation coefficient rho is a measure of this linear relationship. Rho is referred to as R when it is estimated from a sample of data.


Assumption of Pearson's correlation:


N.B. If R is close to ± 1 then this does NOT mean that there is a good causal relationship between x and Y. It shows only that the sample data is close to a straight line. R is a much abused statistic.




