Pearson Correlation
Pearson Correlation is a statistical measure that quantifies the linear relationship between two continuous variables, ranging from -1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no linear correlation. It is widely used in data analysis, machine learning, and research to assess how changes in one variable predict changes in another. The calculation involves covariance and standard deviations, making it sensitive to outliers and only applicable to linear associations.
Developers should learn Pearson Correlation when working with data-driven applications, such as in machine learning for feature selection, data preprocessing, or exploratory data analysis to identify relationships between variables. It is essential in fields like finance for portfolio analysis, in bioinformatics for gene expression studies, and in social sciences for survey data interpretation, helping to inform model building and hypothesis testing.