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In this video i will explain the similarities and differences between correlation, regression and causality. Causality means that there is a clear cause-effect relationship between two variables. A common mistake in the interpretation of statistics is that when a correlation exists it is immediately assumed to be a causal relationship. There are two prerequisites for causality: First, there is a significant relationship, that is, a significant Correlation. The second condition can be satisfied in two ways. First, it is satisfied if there is a temporal ordering of the variables. So variable A was collected temporally before variable B. Furthermore, the second condition can be fulfilled, if there is a theoretically founded and plausible theory in which direction the causal relationship goes. If neither of the two is true, i.e. there is neither a temporal order nor can the causality be justified by a well-founded theory, then we can only speak of a relationship, but never of causality, i.e. it cannot be said that variable A influences variable B or vice versa. More Information about Causality: https://numiqo.com/tutorial/causality Regression Analysis: An introduction to Linear and Logistic Regression • Regression Analysis: An introduction to Li... Simple and Multiple Linear Regression • Simple and Multiple Linear Regression Assumptions of Linear Regression • Assumptions of Linear Regression Logistic Regression: An Introduction • Logistic Regression: An Introduction Dummy Variables in Multiple Regression • Dummy Variables in Multiple Regression Regression with categorical independent variables • Regression with categorical independent va... Multicollinearity • Multicollinearity (in Regression Analysis) Causality, Correlation and Regression • Causality, Correlation and Regression