Correlation vs. Causation

Correlation is a statistical measure that describes the extent to which two variables change together. A positive correlation means that as one variable increases, the other also increases, while a negative correlation means that as one variable increases, the other decreases.

Causation indicates that one event is the result of the occurrence of another event. In other words, causation implies a cause-and-effect relationship between two variables.

A correlation coefficient of -0.87 indicates a strong negative correlation between the two variables being studied.

A correlation coefficient of 0.92 indicates a strong positive correlation between the two variables being studied.

The range of correlation coefficients is from -1 to +1. A coefficient of -1 indicates a perfect negative correlation, 0 indicates no correlation, and +1 indicates a perfect positive correlation.

A correlation coefficient of 0 means that there is no linear relationship between the two variables.

An example of correlation that does not imply causation is the increase in ice cream sales as the temperature rises. While both may increase together, one does not cause the other.

A scatterplot is a graphical representation of two variables where each point represents an observation. It is used to visualize the relationship between the variables.

A strong positive correlation on a scatterplot appears as points that are closely clustered around a straight line that slopes upwards from left to right.

A strong negative correlation on a scatterplot appears as points that are closely clustered around a straight line that slopes downwards from left to right.