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Correlation analysis

Correlation analysis is the measure of the relationship between categorical variables or quantitative variables. It can be used to test the association between two independent variables or between a dependent and an independent variable. There are two types of correlations – positive correlation and negative correlation. Positive correlation: This is an association between two variables whereby both variables move towards the same direction. In other words, when the value of one variable goes higher, the value of the other goes higher as well and vice versa. For instance, a positive correlation could be that the more you study, the higher the marks you will score. Negative correlation: In this relationship, when the value of one variable goes higher, the value of the other goes lower, and vice versa. The correlation analysis studies associations between variables to determine whether a relationship is positive or negative.

Correlation coefficient

The correlation coefficient is the measure of the direction and strength of a linear relationship. The values range from -1 to 1. If the number calculated s less than -1 or greater than 1, it shows that there were some errors in the measurement of the correlation. A value of 1 means that there is a positive correlation between variables. A positive increase in one variable causes a positive increase in the other.  A value of -1 means that there is a negative correlation. This means that the values in variables are moving in opposite directions. A positive increase in one variable causes a negative decrease in the other. If the value is 0, it means that there is no relationship between the directions of both variables being tested.

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 is commonly used in the finance sector and particularly in making financial market investments. For instance, investors can use correlation to find out how a mutual fund will perform relative to another fund, asset class, or its benchmark index. By adding a mutual fund that has a negative correlation to an existing portfolio, investors reap diversification benefits. Simply put, investors can use a negatively correlated asset to improve their portfolio and reduce wild price fluctuations or market risk due to volatility.
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.

Calculating the correlation coefficient

To calculate correlation, you must first identify the covariance of the two variables you want to measure. Once this is done, you must then compute the standard deviation of each variable. The correlation coefficient will be determined by dividing the covariance of both variables by the product of the standard deviations of the two variables. Standard deviation is the extent to which data is dispersed from its average. Covariance on the other hand is the measure of the change in the two variables. These two are essential in determining coefficients and performing correlation analyses. For more information on how to calculate the correlation coefficient, liaise with our correlation analysis homework help experts.
Calculating the correlation coefficient