Relation between two or more characteristics is one of the fundamental queries in development of human thought processes. Such relationship has been studied in different paradigms like causal system, control system, knowledge system. These paradigms are based on different type of believes like some thing is effect of some cause factors (event based causal system) or something may be controlled by some control factor (control system) or some thing may be explained by some explanatory factors (knowledge system). For example, it may be desired to know.
What is the relationship between education and income? For each year of education, how much does income increase (on average)?
What will be the rate of return on investment? For each dollar invested, how much will sales increase?
For a political candidate, how many votes will he get for each unit of money he spends on advertising?
With what confidence, height data may be used for taking decision regarding shoe size? In other words, how much variation in shoe size is explained by height?
With how much confidence we can predict weather on basis of height of barometer?
On basis of data, whether we have sufficient reason to consider parental education level as a cause for maximum level of education of child.
Whether Marginal Propensity to Consume (MPC) is less than 1 as assumed by Keynes?
In statistics there are many tools to get answer based on relationship between characteristics (as in above example) available in form of data. Regression is one of them which works in boundary determined by its assumption and based on concept of dependent characteristics (effect) with independent characteristics (cause) . Although main answer from regression is measure of degree of closeness between cause and effect and change in effect with unit change in cause, it may be used for prediction, validating causal factors, substituting more costly or non available information with set of other information. In fact dependent, and independent characteristics have different name in different framework, see Gujrati (2004) pp 50, (not only `cause’ and `effect’). Although basic statistical tool is same, but due to different frame, different type of answer one may obtained (Schield 1995).
Correlation is close associate of Regression and even widely used than Regression. Regression is a technique while Correlation is a measurement which measure degree of relationship between two variables (may be generalized). Generally speaking, Correlation is a common noun synonymous with ‘association’. In this non-technical sense, Correlation is necessary for causality. But in statistics, Correlation signifies a proper noun -- the Pearson linear product-moment Correlation. In this technical sense, Correlation is not necessary for causality Both concept Correlation and Regression is so much intermingled, that without one it difficult to get understanding of other.
