Regression and Correlation Analysis

Regression and Correlation Analysis
Regression is the statistical technique used in estimating the
relationship between dependent and independent variables. This implies
that the regression analysis helps in determining the trend of change of
the dependent variable whenever one of the independent variable is
varied with the rest of independent variable held constant or fixed.
Regression technique is used in the prediction and forecasting.
Correlation, on the other hand, refers to the statistical technique that
is used to determine the existence of a relationship between variables
and the strength of their relationship if it exists (Hoyt, Leirer &
Millington, 2006).
The relationship between regression and correlation exists because the
prediction made using the regression technique is only possible after
the relationship between variables is established through correlation
technique. For an instant, an economist can predict the rate of growth
in the economy using variables such as rate of unemployment and consumer
spending if he can first establish the existence and the strength of the
relationship between the two variables and economic growth (Hoyt, Leirer
& Millington, 2006). The economist will then determine which of the
variables is the criterion and which one is the predictor variable once
the relationship has been established.
The key differences between the correlation and regression occur in
their application. For instant, regression analysis helps in the
determination of the cause effect relationship while correlation
establishes the relationship between the variables. In addition, the
correlation analysis may be affected by nonsense relationships such as
changes in income and change in weight of a given group of people while
there is no room for nonsense for regression (Anglim, 2007).
There are some circumstances when correlation analysis cannot be viable
in research. For instant, if the research wants to use more than one
predictor variable to forecast metric scaled criterion variables,
regression analysis will be the best option. Regression analysis is
reliable in statistical analysis of multiple variables in a complex
research although correlation analysis forms the basis of meta-analysis
(Anglim, 2007).
Anglim, J. (2007). Correlation, multiple regression, & logistic
regression. London: Sage.
Hoyt, W. Leirer, S. & Millington, M. (2006). Analysis and interpretation
of findings using multiple regression techniques. Rehabilitation
Counseling Bulletin, 49 (4), 223-234.