Correlation Analysis
Correlation analysis is a statistical method used to measure the strength and direction of the linear relationship between two continuous variables. It quantifies how changes in one variable are associated with changes in another, typically using correlation coefficients like Pearson's r. This technique is fundamental in data science, research, and analytics for identifying patterns and dependencies in datasets.
Developers should learn correlation analysis when working with data-driven applications, machine learning models, or statistical reporting to uncover relationships between variables, such as in financial forecasting, user behavior analysis, or feature selection for predictive modeling. It's essential for validating hypotheses, detecting multicollinearity in regression models, and informing data preprocessing decisions in fields like healthcare, marketing, and engineering.