CORRELATION ANALYSIS
Introduction:
In practice, we may come across with lot of situations which need statistical analysis of either one or more variables. The data concerned with one variable only is called univariate data. For Example: Price, income, demand, production, weight, height marks etc are concerned with one variable only. The analysis of such data is called univariate analysis.
The data concerned with two variables are called bivariate data. For example: rainfall and agriculture; income and consumption; price and demand; height and weight etc. The analysis of these two sets of data is called bivariate analysis.
The date concerned with three or more variables are called multivariate date. For example: agricultural production is influenced by rainfall, quality of soil, fertilizer etc.
The statistical technique which can be used to study the relationship between two or more variables is called correlation analysis.
Definition:
Two or more variables are said to be correlated if the change in one variable results in a corresponding change in the other variable.
According to Simpson and Kafka, “Correlation analysis deals with the association between two or more variables”.
Lun chou defines, “ Correlation analysis attempts to determine the degree of relationship between variables”.
Boddington states that “Whenever some definite connection exists between two or more groups or classes of series of data, there is said to be correlation.”
In nut shell, correlation analysis is an analysis which helps to determine the degree of relationship exists between two or more variables.
Correlation Coefficient:
Correlation analysis is actually an attempt to find a numerical value to express the extent of relationship exists between two or more variables. The numerical measurement showing the degree of correlation between two or more variables is called correlation coefficient. Correlation coefficient ranges between -1 and +1.
SIGNIFICANCE OF CORRELATION ANALYSIS
Correlation analysis is of immense use in practical life because of the following reasons:
1. Correlation analysis helps us to find a single figure to measure the degree of relationship exists between the variables.
2. Correlation analysis helps to understand the economic behavior.
3. Correlation analysis enables the business executives to estimate cost, price and other variables.
4. Correlation analysis can be used as a basis for the study of regression. Once we know that two variables are closely related, we can estimate the value of one variable if the value of other is known.
5. Correlation analysis helps to reduce the range of uncertainty associated with decision making. The prediction based on correlation analysis is always near to reality.
6. It helps to know whether the correlation is significant or not. This is possible by comparing the correlation co-efficient with 6PE. It ‘r’ is more than 6 PE, the correlation is significant.