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# Correlation Analysis

Correlation Analysis

The correlation analysis is a statistical method applied in evaluating the relationship between two or more quantitative variables. A high correlation between variables implies that the their relationship is high, while a low correlation implies that the relationship is low. In other words, correlation analysis is a technique of studying the strength of the relationship between variables. The technique is highly associated with regression analysis, which is a technique that evaluates the association between the dependent and the independent variable. It a correlation is found between some variables, then it implies that a change in one varibles leads to a changes in another variable.

Categories of correlation analysis

Based on the numerical values measured, the correlation could positive or negative.

Positive correlation: is the correlation value is positive, it implies that an increase in a variable would result to simultaneously increase in the other.

Negative correlation: a negative correlation implies that if one variable increases, the other variable decreases and vice versa.

The correlation could be summarised using the figure below.

The Pearson product moment correlation is a measurement which ranges between positive one and negative one (+1 and _1).  +1 implies that there is a strong positive correlation between variables, while -1 implies that there is a strong negative correlation between the variables considered. This implies that the close the correlation results to these values, the stronger the correlation of the variables. On the other hand, 0 indicated that there is no correlation between variables. Therefore, the close the correlation value is to 0, the weaker or poor the correlation is between the considered variables.

Interpretation of Correlation Analysis

The sign of the correlation indicates the direction of the relationship between the variables considered. The magnitude of the correlation coefficient indicates the strength of the association between the variables. The sample correlation coefficient is denoted by r. for instance, a correlation coefficient of r=0.9 indicates a strong and positive relationship between the two variables considered. On the other hand, a correlation of r=-0.2 indicates that there is a weak and negative correlation between the variables. It is important to note that graphical representation is vital to explicitly shoe the relationship between variables. this is shown by the figure below.

The figure above shows one variable plotted on y-axis and another plotted on x axis. Scenario one shows a strong association between the variables e.g. r = 0.9. scenario 2 shows a weaker correlation between the variables considered e.g r=o.2. scenario 3 shows lack of correlation between the variables considered. Lastly, scenario 4 shows strong and negative correlation between the variables considered.