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Causality, Correlation and Regression

This video will explain you the commonalities and differences between the correlation, regression and the 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 a causality is inferred. 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://datatab.net/tutorial/causality Regression Analysis: An introduction to Linear and Logistic Regression    • Regression Analysis: An introduction ...   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 independe...   Multicollinearity    • Multicollinearity (in Regression Anal...   Causality, Correlation and Regression    • Causality, Correlation and Regression