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Perhaps the most common goal in statistics is to answer the question: Is the variable X (or more likely, X 1, ..., X p) associated with a variable Y, and, if so, what is the relationship and can we use it to predict Y?

Nowhere is the nexus between statistics and data science stronger than in the realm of prediction-specifically the prediction of an outcome (target) variable based on the values of other "predictor" variables. Another important connection is in the area of anomaly detection, where regression diagnostics originally intended for data analysis and improving the regression model can be used to detect unusual records. The antecedents of correlation and linear regression date back over a century.