Summary
Highlights
Correlation is a statistical tool used to understand the relationship between different variables. For instance, it can be used to analyze the relationship between the amount of rain received and the number of raincoats sold, which can be visualized on a scatter plot.
The strength of a relationship between variables is quantified by the correlation coefficient, denoted as 'r'. This value always falls between -1 and 1. A value of -1 indicates a strong negative correlation (as one variable goes up, the other goes down), while 1 indicates a strong positive correlation (both variables go up together). A value near 0 suggests little to no correlation.
An example using LeBron James's rookie season data explores the correlation between his minutes per game and field goal percentage. A scatter plot and a correlation coefficient of 0.353 indicate a positive but somewhat weak relationship.
Another example examines the relationship between four cement ingredients and the heat evolved during its formation. A correlation matrix is used to analyze these multiple variables, showing some strong positive and negative correlations between ingredients and heat evolution.
A crucial point to remember is that correlation does not imply causation. Just because two variables are related doesn't mean that a change in one directly causes a change in the other. Experiments are needed to establish causation.