Regression analysis refers to any kind of technique used in modeling and evaluation of variables especially when the center of analysis lies between dependent variables and several independent variables. More expressly, regression assists tacticians to comprehend how the characteristic value of the dependent variable changes with variation in either of the independent variable when the other independent variables in the systems are held fixed (Miles and Shevlin, 2001). Normally, the application of regression analysis lies in estimation and forecasting of what is likely to occur particularly during machine training exercises.
Regression analysis is also applied in evaluations of which independent variables are interrelated with the dependent variables, and to investigate the kind of relation. In formulation of strategies, regression is widely used in data analysis whereby it assists in coming up with statistical procedures and programs that guide study, various strategies may be compared on their performance once the criteria for evaluating results is has been formulated (Miles and Shevlin, 2001). During regression, forecasting and estimation of the condition to be expected out of the dependent variable having been given the independent variable is carried out i.e. the standard value of the dependent variable after the independent variable remain fixed as this will give the estimated target putting the quartile and other parameters of distribution in consideration.
Correlation analysis reflects the degree to which two quantitative variables differ jointly counting on the strength and direction of their correlation. In this context, the extent that a variable predicts the other is the strength of that relationship. For instance, in the evaluation of customer, it might be revealed the expenses on groceries is directly proportional to the size of the household, this could be a positive correlation but a weak correlation can also be realized from the customer approval survey and weekly expense on groceries (Bobko, 2001). The direction of correlation reflects whether two variables differ jointly proportionally or indirectly. In the case of a direct relationship, the two variables add up jointly whereas in inverse relation, one variable tends to reduce as the other one increase in figure.
Indeed Correlation analysis can be applied in making conclusions relating to one variable of which can’t be measured easily. For instance, product sales can’t be measured when the product has not been produced or marketed. Correlation analysis of related commodities may demonstrate to us the variables which influence sales.
How may correlation analysis be misused to explain a cause-and-effect relationship? The relationship between independent and dependent variables concerning correlation and regression analysis.
Correlation analysis can mean an underlying relationship where there is the inexistence of one. Assumptions can easily be made that if there is a correlation of two variables then one is caused by the other, but it is possible that the two variables could be associated for other reasons. For instance, research might discover a strong positive correlation amid the sum of which parents and their teenage children smoke. On the other hand, with supplementary, it’s impracticable to understand whether other aspects are casual, i.e. parental edification level, natural features, genetics or household income (Miles and Shevlin, 2001). Indeed correlation analysis is vital for finding if variables differ jointly or disjointedly, in whatever dimension, and of what strength.
Independent variable and dependent variable are expressions equally but finely applied differently in math and statistics as elements of the terminology in these two subjects. They differentiate two types of quantities being measured, sorting out those available from the beginning of the process and those being formed by it where dependent variables are variable on the independent variable. In regards to correlation and regression analysis, they’re used statistical tools are used to evaluate relationships between different quantities. For instance, correlation and regression define the uncertainty between any values of an independent variable as compared to the value of a dependent variable (Bobko, 2001).
Miles. J, & Shevlin. M (2001). Applying regression & correlation: a guide for students and researchers, Publisher SAGE.
Bobko. P (2001). Correlation and regression: applications for industrial organizational psychology and management Organizational Research Methods. Publisher Sage Publications.