Karl Pearson’s Coefficient of Correlation, often simply referred to as Pearson’s correlation coefficient, is a measure of the linear relationship between two variables. It ranges from -1 to 1, where:
1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, 0 indicates no linear relationship. Using the assumed mean method, we can simplify the calculations, especially when dealing with large datasets. Here’s a detailed breakdown of the process:
Steps to Calculate Karl Pearson’s Coefficient of Correlation Using Assumed Mean
1. Assumed Mean Method Basics:
The assumed mean method involves selecting a convenient value (assumed mean) to simplify the calculations. This is particularly useful when dealing with large numbers, as it reduces the magnitude of the numbers we work with. kindly see the link for simplified solution :
It concludes that The assumed mean method simplifies calculations of Pearson’s correlation coefficient, making it easier to handle large datasets. It provides the same result as using the actual means but with reduced computational
