Tips for Correlation Analysis
- Data Cleaning: Make sure that your data is accurate and error-free before performing the correlation analysis. Incorrect or missing data can affect the output.
- Sample Size: Correlation analysis is more reliable with larger sample sizes. Smaller sample sizes may lead to less accurate results.
- Causation vs. Correlation: Correlation does not imply causation. Even with a strong correlation, it is essential to explore other factors and conduct further research before establishing causation.
How to Calculate Correlation in Excel
Correlation is a concept that hails from the statistics background. In statistical terms, correlation can be defined as the linear association between two entities. Simply, it can be understood as the change in one entity leads to how much proportion changes in another entity. Many times’ correlation is often confused with another popular term in statistics, Causation. To differentiate and clarify, one must understand, correlation does not cause a change in the values of the second entity when the values of the first entity change and vice-versa.
Let’s understand this difference with the help of an example. It has been often observed that during the summer season crime rates usually increase in a city and also during the summer season there is an increase in the sale of ice cream. We can easily understand that due to the increase in temperature, people tend to prefer cooler food items for relaxation from heat, thus it causes an increase in ice cream sales. Thus, this is a common cause of Causation, whereas when we compare the increase in the sale of ice cream to the increase in crime rate during summer, both are correlated, but one is not the cause of another.
Now, there can be either a positive correlation or a negative correlation between two entities. The degree of correlation is often given using a correlation coefficient named as Pearson Correlation coefficient, which is named after Karl Pearson, who gave the concept of Correlation. The statistical formula for Pearson’s coefficient is given as:
Where x and y are two separate entities, Cov(x,y) is the covariance between two entities x and y, σx and σy is the standard deviation of x and y respectively. To know more about the mathematical equation and how it is used, you can refer to https://www.w3wiki.org