Youtor
Causality Correlation and Regression
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=dhCnAO4UoiM
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
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