Correlation is a method used to establish the relationship between two variables. The correlation coefficient, which is a single number ranging from -1 to +1, summarizes the strength and direction of the relationship between the variables.
A correlation coefficient close to zero, either positive or negative, suggests a weak or no relationship between the variables. On the other hand, a correlation coefficient close to +1 indicates a positive relationship, where increases in one variable correspond to increases in the other variable.
A correlation coefficient close to -1 suggests a negative relationship, where increases in one variable correspond to decreases in the other variable. The correlation coefficient can be calculated for variables measured at the ordinal, interval, or ratio level, but it has little significance for variables measured nominally.
Examples of positive correlation are:
Examples of negative correlation are:
Methods of estimating correlation:
A scatter plot provides a visual representation of the strength and direction of correlation between two variables. It involves plotting the x variable on the X-axis and the y variable on the Y-axis, resulting in a cluster of points called a scatter plot. The proximity and overall direction of the scatter points help us to analyze the relationship between the two variables.
Karl person’s coefficient of correlation is a quantitative method of calculating correlation. It gives a precise numerical value of the degree of linear relationship between two variables.
Karl person’s coefficient of correlation is also known as product moment correlation.
Formula:
Here,
r = Coefficient of correlation
σx = Standard deviation of X-series.
σy = Standard deviation of Y-series.
N = Number of observations
Spearman's rank correlation coefficient is a useful tool for determining the correlation between variables that may not have a clear or objective measurement. It is particularly helpful when analyzing the correlation of qualitative variables.
Here,
rs = coefficient of rank correlation
D = Rank differences
N = Number of rank
When ranks are repeated the formula is:
Where m1, m2, .............. are number of repetitions of ranks.
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