Simple Linerar Regeression and Correlation

In analyzing data for the health sciences disciplines, we find that it is frequently desireable to learn something about the relationship between two variables. We may, for example, be intrested in studying the relationship between blood pressure and age, height and weight, the concentration of an injected drug and heart rate, the consumption level of some nutrient and weight gain, the intensity of a stimulus and reaction time, or total family income and medical care expenditures. the nature and strength of the relationship between variables such as these may be examined by Regression and Correlation analysis, two statistical techniques that, although related, serve different purposes.

Regression

regression analysis is helpful in ascertaining the probable form of relationship between variables, and the ultimate objective when this method of analysis is employed usually is to predict or estimate the value of one varianle corresponding to a given value of another variable. The ideas of regression were first elucidated by the English Scientist Sir Francis Galton in reports of his research on heridity ( firstly in the sweet peas and lator in human stature. He described a tndency of adult offspring, having either shortb or tall parens, to revert back toward the average height of general population.He first used the word reversion, and later regression, to refer to this phenomenon.

Correlation

on the other hand, correlation is concerd with measuring the strength of the ralationship between variables. When we compute measures of correlation from a set of data, we are interested in the degree of the correlation between variables. The concept and the terminology of correlation analysis originated with Galton, who first used the word correlation.