Statistics · Data presentation
Chapter 1 · 4
The idea
Correlation and regression
Bivariate data and scatter diagrams; describing correlation by its direction (positive/negative) and strength; why correlation does not imply causation; and using a given regression line y = a + bx to make and interpret predictions — including why prediction inside the data range (interpolation) is safe but outside it (extrapolation) is not. Interpreting the PMCC is flagged as A-level.
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Statistics · Data presentation
Correlation and regression
Bivariate data and scatter diagrams; describing correlation by its direction (positive/negative) and strength; why correlation does not imply causation; and using a given regression line y = a + bx to make and interpret predictions — including why prediction inside the data range (interpolation) is safe but outside it (extrapolation) is not. Interpreting the PMCC is flagged as A-level.
Why it works
Data in pairs
So far each value stood alone. Bivariate data comes in pairs — a temperature and the ice-cream sales that day — and the question becomes whether the two move together. Plot each pair as a point on a scatter diagram and the shape of the cloud tells you.Direction and strength
Correlation describes a linear tendency in that cloud, in two parts:- Direction — positive correlation if tends to rise as rises (points trend up to the right); negative if tends to fall as rises.
- Strength — strong if the points lie close to a straight line, weak if they are loosely scattered. If there is no linear pattern, there is no correlation.
Keep reading — free
The rest of the explanation, plus 4 worked examples you step through move by move.
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