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://datatab.net/tutorial/causality
Regression Analysis: An introduction to Linear and Logistic Regression
https://youtu.be/FLJ0yYetywE
Simple and Multiple Linear Regression
https://youtu.be/29rjWClT_3U
Assumptions of Linear Regression
https://youtu.be/sDrAoR17pNM
Logistic Regression: An Introduction
https://youtu.be/3tq4t41MsPc
Dummy Variables in Multiple Regression
https://youtu.be/bnjPzHQ04Ac
Regression with categorical independent variables
https://youtu.be/xVBwXqnWPyE
Multicollinearity
https://youtu.be/G1WX5GiFSWQ
Causality, Correlation and Regression
https://youtu.be/dhCnAO4UoiM