yet another post for data analytics students
In the world of data analysis, we often want to know if two things are related. This is where correlation comes in. Simply put, correlation tells us if there’s a relationship between two variables: when one changes, does the other tend to change as well? This relationship can be positive, meaning both variables increase together (like the number of customers and the number of sales), or negative, meaning as one variable increases, the other tends to decrease. We can often get a rough idea of correlation by looking at a scatter plot.
To get a more precise measure of this relationship, we use the correlation coefficient, often denoted as ‘R’. This value ranges from -1 to +1 .
- • An R close to +1 indicates a strong positive correlation.
- • An R close to -1 indicates a strong negative correlation.
- • An R close to 0 suggests a weak or no linear correlation between the variables.
While R tells us the strength and direction of the linear relationship, another important metric is R-squared (R²), also known as the coefficient of determination. You get R-squared by simply squaring the correlation coefficient (R). R-squared tells you the proportion of the variance in one variable that is predictable from the other variable. For example, an R-squared of 0.50 means that 50% of the variation in your dependent variable can be explained by the variation in your independent variable.
It’s crucial to remember one vital point: correlation does not equal causation6 . Just because two variables are correlated, it doesn’t mean that changes in one variable cause changes in the other. There might be a third, unseen factor at play, or the relationship could be coincidental. Correlation analysis is a valuable tool for identifying relationships between two numerical variables, but further investigation is needed to understand the underlying reasons for these connections.