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:
datatab.net/tu...
Regression Analysis: An introduction to Linear and Logistic Regression
• Regression Analysis: A...
Simple and Multiple Linear Regression
• Simple and Multiple Li...
Assumptions of Linear Regression
• Assumptions of Linear ...
Logistic Regression: An Introduction
• Logistic Regression: A...
Dummy Variables in Multiple Regression
• Dummy Variables in Mul...
Regression with categorical independent variables
• Regression with catego...
Multicollinearity
• Multicollinearity (in ...
Causality, Correlation and Regression
• Causality, Correlation...
Негізгі бет Causality, Correlation and Regression
No video
Пікірлер: 32