18.1.4

Correlation

Test yourself

Correlation and Causation

Correlation means that there is a relationship between two variables. Causation means that changing one causes the other to change.

Illustrative background for CausationIllustrative background for Causation ?? "content

Causation

  • Correlation does not imply causation.
  • If two variables are correlated that does not mean that changes in one directly cause changes in the other.
Illustrative background for ExampleIllustrative background for Example ?? "content

Example

  • The number of ice cream sales might be positively correlated with temperature but this does not mean that ice cream sales cause higher temperatures.

Pearson's Linear Correlation

Two variables are correlated if you can draw a straight line that lies close to most of the points.

Illustrative background for Variables and constantsIllustrative background for Variables and constants ?? "content

Variables and constants

  • The variable x is the independent or explanatory variable.
  • The variable y is the dependent or response variable.
  • The slope of the regression line is given by the constant b.
    • The sign and size of this number tell us how y changes for every unit increase in x on average.
Illustrative background for Pearson's correlation coefficientIllustrative background for Pearson's correlation coefficient ?? "content

Pearson's correlation coefficient

  • The Pearson's correlation coefficient, rr, provides a measure of strength and direction of the correlation between x and y.
  • The value of rr is always between –1 and +1.
Illustrative background for Pearson's correlation coefficientIllustrative background for Pearson's correlation coefficient ?? "content

Pearson's correlation coefficient

  • Values of rr close to –1 or to +1 indicate a stronger linear relationship between x and y.
    • If r=0r = 0, there is likely no linear correlation.
    • If r=1r = 1, there is a perfect positive correlation so all points lie on a straight line.
    • If r=-1r = \text{-}1, there is a perfect negative correlation so all points lie on a straight line.
Illustrative background for FormulaIllustrative background for Formula ?? "content

Formula

  • The formula for the correlation coefficient is:
    • r=1n1(xixsx)(yiysy)r = \frac{\large 1}{\large n-1}\sum\left(\frac{\large x_i-\overline{x}}{\large s_x}\right)\left(\frac{\large y_i-\overline{y}}{\large s_y}\right)
  • Where x,y\overline{x},\overline{y} are the mean and sx,sys_x,s_y are the standard deviations of the explanatory and response variables respectively.
  • It is much more common to calculate rr using a calculator.

Jump to other topics

1Cell Structure

2Biological Molecules

3Enzymes

4Cell Membranes & Transport

5The Mitotic Cell Cycle

6Nucleic Acids & Protein Synthesis

7Transport in Plants

8Transport in Mammals

9Gas Exchange

10Infectious Diseases

11Immunity

12Energy & Respiration (A2 Only)

13Photosynthesis (A2 Only)

14Homeostasis (A2 Only)

15Control & Coordination (A2 Only)

16Inherited Change (A2 Only)

17Selection & Evolution (A2 Only)

18Classification & Conservation (A2 Only)

19Genetic Technology (A2 Only)

Unlock your full potential with Seneca Premium

  • Unlimited access to 10,000+ open-ended exam questions

  • Mini-mock exams based on your study history

  • Unlock 800+ premium courses & e-books

Get started with Seneca Premium