GeographyStatistical Techniques and Data AnalysisA-Level

Pearson's Product-Moment Correlation Coefficient Calculator

A statistical measure that quantifies the strength and direction of the linear relationship between two continuous interval or ratio variables.

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Formula first

Overview

Pearson's r produces a value between -1 and +1, where +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear correlation. In geographical research, it is essential for testing hypotheses about how two variables, such as distance from a CBD and property prices, covary across a landscape. The coefficient assumes that the data is normally distributed and that the relationship is strictly linear.

Symbols

Variables

r = Correlation Coefficient, n = Sample size, x = Variable 1 data points, y = Variable 2 data points

Correlation Coefficient
Variable
Sample size
Variable
Variable 1 data points
Variable
Variable 2 data points
Variable

Apply it well

When To Use

When to use: Use when analyzing two sets of interval or ratio data to determine if a linear trend exists between them.

Why it matters: It allows geographers to move beyond visual inspection of scatter graphs to provide a statistically significant confirmation of relationships between environmental or social variables.

Avoid these traps

Common Mistakes

  • Forgetting to square the sum (Σx)² versus summing the squares Σx².
  • Applying the test to non-linear relationships (e.g., exponential growth patterns).
  • Ignoring the impact of extreme outliers which can heavily bias the result.

One free problem

Practice Problem

Given a small sample where n=5, Σx=15, Σy=20, Σxy=70, Σx²=55, and Σy²=90, calculate Pearson's r.

Sample size5
sumXY70
sumX15
sumY20
sumX255
sumY290

Solve for:

Hint: Calculate the numerator first, then the denominator parts separately.

The full worked solution stays in the interactive walkthrough.

References

Sources

  1. Pearson, K. (1896). Mathematical Contributions to the Theory of Evolution.
  2. Burt, J. E., Barber, G. M., & Rigby, D. L. (2009). Elementary Statistics for Geographers.
  3. AQA/Edexcel A-Level Geography Specification - Quantitative Skills: Statistical Analysis