How to use the geometric mean population growth calculator
Find the average annual multiplication factor of a population from a series of counts. Enter population counts over time in the field above; the geometric mean annual multiplier, geometric mean growth per interval are computed instantly and plotted on the chart so you can see how the answer changes.
Common searches this calculator answers include “geometric mean growth rate”, “average population multiplier”, “geometric mean of ratios”, “calculate geometric mean population growth”, “find geometric mean annual multiplier”.
Formula
Variables
- n — Population counts over time
Worked example
With population counts over time = 10,15,22,33,50, the calculator gives geometric mean annual multiplier ≈ 1.495349; geometric mean growth per interval ≈ 49.53488 %.
Frequently asked questions
What is the geometric mean population growth calculator used for?
Find the average annual multiplication factor of a population from a series of counts. It helps with questions such as “geometric mean growth rate”, “average population multiplier”, “geometric mean of ratios”.
What is the formula for the geometric mean population growth calculation?
Geometric mean annual multiplier = geomean(n.slice(1).map(function(v,i){return v / n[i]})). Geometric mean growth per interval (%) = ([Geometric mean annual multiplier]-1) × 100. Here n is population counts over time.
What inputs do I need for the geometric mean population growth calculator?
You need population counts over time. Each field is pre-filled with a typical example so you can see how the result responds as you edit the numbers.
How do I use the geometric mean population growth calculator?
Type values into the input fields. Results and the chart update instantly. Use Reset to restore the example values, Copy results to paste them elsewhere, or Share to copy a link that preserves your inputs.
Is the geometric mean population growth result exact?
Calculations use standard formulas and double-precision arithmetic, then display about seven significant figures. Real-world measurements carry uncertainty, so treat results as estimates unless your inputs are exact.