Correlation analysis is a Statistical technique that used to measure and determine the relationship between the two variables. Example decline in rice production due to reduced fertilizer, declining sales results may be due to the decline in advertising costs, rise in blood pressure due to weight gain. The relationship between the two variables there are positive and negative. For example, the relationship between variables X and Y. The relationship of X and Y are positive if rise in X followed by the increase in Y and otherwise. The relationship between variables X and Y are said to be negative if the increase followed by a decrease X Y and otherwise.
Examples of positive relationships:
X = Fertilizer Y = Produced
X = Cost of Ad Y = Income Sales
X = Body Weight Y = Blood Pressure
Example of a negative relationship:
X = Price of Goods Y = Demand
Relationship between two variables measured by a value called the correlation coefficient. Correlation coefficient represent the linear dependence of two variables or sets of data.The correlation coefficient ranges between -1 and 1, the correlation coefficient is denoted by (r), the value of r can be written as follows: -1 < r < 1
- r = 1, meaning that the relationship between the two variables and positively perfect (close to 1, the relationship is very strong and positive).
- r = -1, Meaning that the relationship between the two variables and negative perfect (close to -1, the relationship is very strong and negative).
- r = 0, the relationship between the two variables is weak even none relationship
The correlation coefficient is defined as follows :
the contribution of X to Y is calculated with a coefficient called the coefficient of determination that denoted by (CD). Coefficient of determination is defined as follows :
Sample Case of the Correlation Analysis
Suppose X is the cost of advertising, while Y is the income sale, Calculate the correlation coefficient (r) and coefficient of determination of the two variables.
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