There are many different types of correlation coefficients. The most common, however, is the Pearson correlation, usually denoted as r. The Pearson’s coefficient studies the direction and strength of the relationship between two variables. It is only used to capture linear relationships; it cannot be used for nonlinear relationships. Also, the Pearson’s coefficient cannot differentiate between independent and dependent variables.
The strength of a linear relationship varies based on the correlation coefficient value. For instance, a value of 0.3 means that there is a positive relationship between the two variables being observed. However, this is considered a weak and important correlation. In some fields of study, analysts do not consider correlations strong enough until the value has surpassed at least 0.7. A correlation value close to 1.0 represents a completely strong relationship. To understand correlation analysis and how it is used to measure the direction and strength of a relationship, connect with our correlation analysis online tutors.
Application of correlation analysis in real-life
Correlation analysis also enables investors to find out when the correlation between variables has changed. For instance, bank stocks usually have a high positive correlation to loan interest rates since these rates are calculated on the basis of the market interest rates. If the price of stocks of a given bank is decreasing while the interest rates are increasing, investors will automatically know that something is misaligned. If the price of the stock of other banks in the same sector is increasing with the increase in the loan rates, then the investors can conclude that the bank stock that is declining is not doing so due to interest rates. The decline could be caused by an internal fundamental issue.