{"id":12450,"date":"2023-10-11T14:59:44","date_gmt":"2023-10-11T09:29:44","guid":{"rendered":"https:\/\/skillioma.com\/learn\/courses\/ai-ml-nascomm-fsp\/lesson\/relationship-between-variables-regression-correlation-covariance-2\/"},"modified":"2024-02-02T16:15:14","modified_gmt":"2024-02-02T10:45:14","slug":"relationship-between-variables-regression-correlation-covariance-2","status":"publish","type":"lesson","link":"https:\/\/skillioma.com\/learn\/courses\/ai-ml-and-data-science-foundation-nascomm-fsp\/lesson\/relationship-between-variables-regression-correlation-covariance-2\/","title":{"rendered":"Relationship Between Variables – Regression \/ Correlation \/ covariance"},"content":{"rendered":"

Regression, Correlation, and Covariance are related statistical concepts, each serving specific purposes, but they also inform one another.<\/span><\/p>\n

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Regression:<\/b><\/h4>\n

Regression is a statistical analysis used to predict the value of one variable based on the value(s) of one or more other variables. The most common form of regression is linear regression.<\/span><\/p>\n

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Linear Regression Formula (Simple Linear Regression):<\/b><\/p>\n

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Correlation:<\/b><\/h4>\n

Correlation measures the strength and direction of a linear relationship between two variables. It gives a value between -1 and 1.<\/span><\/p>\n