Regression vs correlation and causation

A simple explanation of Regression | Regression versus Causation | Regression versus Correlation
The presentation aims at explaining the basic concept of regression. It also shows how regression is different from causation and correlation.
For further explanation, checkout the youtube link: https://youtu.be/SELNQs9b-XY

1. What is Regression? Regression versus Causation Regression versus Correlation

2. Regression Francis Galton Regression analysis is concerned with the study of the dependence of one variable on one or more other variables with a view to estimating and/or predicting the population mean or average value of the former in terms of the known values of the latter.

3. Example: Predicting the average height of sons knowing the height of their fathers

4. Regression Versus Causation Causation  indicates a relationship between two variables where one variable is affected by the other. Y = f(Rain) Rain = f(Y)

5. Regression Versus Correlation Correlation Regression Correlation is a statistical measure which determines co-relationship or association of two variables. Regression describes how an independent variable is numerically related to the dependent variable. To represent linear relationship between two variables. To fit a best line and estimate one variable on the basis of another variable. No difference between dependent and independent variables. Both variables are different. Correlation coefficient indicates the extent to which two variables move together. Regression indicates the impact of a unit change in the known variable (x) on the estimated variable (y). To find a numerical value expressing the relationship between variables. To estimate values of random variable on the basis of the values of fixed variable.

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