Data Correlation Analysis

Introduction:
The analysis of correlation in a bivaraite data is called Data Analysis Correlation. In the bivariate data if the change in the value of one variable is accompained by the change in the value of the other , then the variables are referred as related variables and this relation is called as correlation.

Correlation is an arithmetic technique to be able to show whether along with how powerfully pairs of variables be associated. Example, height and weight be associated; taller people be inclined toward exist heavier than shorter people.Even though this correlation is practically understandable your data can include unsuspected correlations. You may well also believe here are correlations, except don't recognize which are the strongest. An intelligent correlation analysis be able to lead to a greater accepting of your data.

Data Correlation Analysis for Correlation Coefficient

The major result of a correlation is calling the correlation coefficient "r". It ranges since 0 to +1.0. The nearer r is to +1 or -1, the further closely the two variables are associated. But r is close to 0, it way here is no connection between the variables.

If r is positive, it means to as one variable get larger the other gets larger. If r is negative it means to when one gets larger, the further gets smaller (frequently called an "inverse" correlation.

Examples -

1. Increase in the weight of a man with increase in age upto a definite period of time.
2. Fall in price of a commodity with rise in production or supply.
3. Increase in expenses with an increase in income.

Hope you liked the above explanation. Please leave your comments, if you have any doubts.