 | This table may be more easily updated if the rank-order column (1,2,3) is removed and a row number column is added instead. Alphabetical order may also help. See examples here . |
This is a list of member states of the Commonwealth of Nations by population, which is sorted by the 2015 mid-year normalized demographic projections.
| Rank | Country (or dependent territory) | July 1, 2015 projection [1] | % of pop. | Average relative annual growth (%) [2] | Average absolute annual growth [3] | Estimated doubling time (Years) [4] | Official figure (where available) | Date of last figure | Source |
|---|---|---|---|---|---|---|---|---|---|
| 1 |  India | 1,299,499,000 | 55.12 | 1.64 | 20,998,000 | 43 | 1,405,530,000 | July28, 2024 | Official population clock |
| 2 |  Pakistan | 191,785,000 | 8.14 | 2.00 | 3,765,000 | 35 | 238,088,000 | July28, 2024 | Official population clock |
| 3 |  Nigeria | 184,264,000 | 7.82 | 2.91 | 5,205,000 | 24 | 174,000,000 | 2013 | Official estimate |
| 4 |  Bangladesh | 158,762,000 | 6.73 | 1.37 | 2,139,000 | 51 | 177,250,000 | July28, 2024 | Official population clock |
| 5 |  United Kingdom | 65,093,000 | 2.76 | 0.77 | 495,000 | 91 | 64,596,800 | June 30, 2014 | Official estimate |
| 6 |  South Africa | 54,957,000 | 2.33 | 1.61 | 873,000 | 43 | 54,956,900 | July 1, 2015 | Official estimate |
| 7 |  Tanzania | 48,829,000 | 2.07 | 2.97 | 1,407,000 | 24 | 47,421,786 | 2014 | Official estimate |
| 8 |  Kenya | 44,234,000 | 1.88 | 2.87 | 1,234,000 | 24 | 43,000,000 | 2014 | Official estimate |
| 9 |  Canada | 35,819,000 | 1.52 | 0.79 | 279,000 | 89 | 41,328,500 | July28, 2024 | Official estimate |
| 10 |  Uganda | 35,760,000 | 1.52 | 3.09 | 1,071,000 | 23 | 34,856,813 | August 28, 2014 | Preliminary 2014 census result |
| 11 |  Malaysia | 31,032,000 | 1.32 | 1.84 | 561,000 | 38 | 34,113,100 | July28, 2024 | Official population clock |
| 12 |  Ghana | 27,714,000 | 1.18 | 2.48 | 671,000 | 28 | 27,043,093 | 2014 | Official estimate |
| 13 |  Mozambique | 25,728,000 | 1.09 | 2.74 | 686,000 | 26 | 25,727,911 | 2015 | Official estimate |
| 14 |  Australia | 23,792,000 | 1.01 | 1.42 | 333,000 | 49 | 27,321,700 | July28, 2024 | Official population clock |
| 15 |  Cameroon | 21,918,000 | 0.93 | 2.65 | 565,000 | 27 | 21,917,602 | 2015 | Official estimate |
| 16 |  Sri Lanka | 20,869,000 | 0.89 | 0.94 | 194,000 | 74 | 20,771,000 | July 1, 2014 | Official estimate |
| 17 |  Malawi | 16,307,000 | 0.69 | 3.18 | 502,000 | 22 | 16,832,900 | July 1, 2016 | Official estimate |
| 18 |  Zambia | 15,474,000 | 0.66 | 3.00 | 451,000 | 23 | 15,473,905 | 2015 | Official estimate |
| 19 |  Rwanda | 11,324,000 | 0.48 | 2.61 | 288,000 | 27 | 10,515,973 | August 15, 2012 | Final 2012 census result |
| 20 |  Papua New Guinea | 8,219,000 | 0.35 | 3.11 | 248,000 | 23 | 7,744,700 | 2015 | Official estimate |
| 21 |  Sierra Leone | 6,513,000 | 0.28 | 2.57 | 163,000 | 27 | 6,348,350 | 2014 | Official estimate |
| 22 |  Singapore | 5,541,000 | 0.24 | 1.30 | 71,000 | 54 | 5,535,000 | July 1, 2015 | Official estimate |
| 23 |  New Zealand | 4,579,000 | 0.19 | 1.53 | 69,000 | 46 | 5,385,180 | July28, 2024 | Official population clock |
| 24 |  Jamaica | 2,729,000 | 0.12 | 0.26 | 7,000 | 270 | 2,723,246 | December 31, 2014 | Official estimate |
| 25 |  Namibia | 2,281,000 | 0.10 | 2.01 | 45,000 | 35 | 2,280,700 | July 1, 2015 | Official estimate |
| 26 |  Botswana | 2,176,000 | 0.09 | 1.92 | 41,000 | 36 | 2,024,904 | August 22, 2011 | Final 2011 census result |
| 27 |  Gambia | 2,022,000 | 3.27 | 64,000 | 22 | 2,101,000 | July 1, 2017 | UN projection | |
| 28 |  Lesotho | 1,908,000 | 0.08 | 0.21 | 4,000 | 330 | 1,894,194 | 2011 | Official estimate |
| 29 |  Trinidad and Tobago | 1,357,000 | 0.06 | 0.52 | 7,000 | 134 | 1,349,667 | 2015 | Official estimate |
| 30 |  Mauritius | 1,263,000 | 0.05 | 0.16 | 2,000 | 437 | 1,261,208 | July 1, 2014 | Official estimate |
| 31 |  Eswatini (Swaziland) | 1,119,000 | 0.05 | 1.18 | 13,000 | 59 | 1,119,375 | 2015 | Official estimate |
| 32 |  Fiji | 867,000 | 0.04 | 0.46 | 4,000 | 150 | 867,000 | 2015 | Official estimate |
| 33 |  Cyprus | 846,000 | 0.04 | -0.94 | -8,000 | - | 858,000 | December 31, 2013 | Official estimate |
| 34 |  Guyana | 747,000 | 0.03 | 0.00 | 0 | - | 747,884 | September 15, 2012 | Preliminary 2012 census result |
| 35 |  Solomon Islands | 587,000 | 0.02 | 2.26 | 13,000 | 31 | 642,000 | 2015 | Official estimate |
| 36 |  Malta | 425,000 | 0.02 | 0.47 | 2,000 | 147 | 417,432 | November 20, 2011 | 2011 census result |
| 37 |  Brunei | 421,000 | 0.02 | 1.69 | 7,000 | 41 | 393,162 | June 20, 2011 | Preliminary 2011 census result |
| 38 |  Bahamas | 379,000 | 0.02 | 1.34 | 5,000 | 52 | 369,670 | 2015 | Official estimate |
| 39 |  Belize | 369,000 | 0.02 | 2.50 | 9,000 | 28 | 368,310 | June 2015 | Official estimate |
| 40 |  Barbados | 283,000 | 0.01 | 0.35 | 1,000 | 196 | 277,821 | May 1, 2010 | 2010 census result |
| 41 |  Vanuatu | 278,000 | 0.01 | 2.58 | 7,000 | 27 | 277,600 | 2015 | Official estimate |
| 42 |  Samoa | 193,000 | 0.01 | 0.52 | 1,000 | 133 | 194,899 | 2016 | Official estimate |
| 43 |  Saint Lucia | 172,000 | 0.01 | 0.58 | 1,000 | 119 | 166,526 | May 10, 2010 | Preliminary 2010 census result |
| 44 |  Kiribati | 113,000 | 0.00 | 1.80 | 2,000 | 39 | 113,400 | 2015 | Official estimate |
| 45 |  Saint Vincent and the Grenadines | 110,000 | 0.00 | 0.00 | 0 | - | 109,434 | 2014 | Official estimate |
| 46 |  Grenada | 104,000 | 0.00 | 0.00 | 0 | - | 103,328 | May 12, 2011 | Preliminary 2011 census result |
| 47 |  Tonga | 104,000 | 0.00 | 0.00 | 0 | - | 103,300 | 2015 | Official estimate |
| 48 |  Seychelles | 97,000 | 0.00 | 1.04 | 1,000 | 67 | 90,945 | August 26, 2010 | Final 2010 census result |
| 49 |  Antigua and Barbuda | 89,000 | 0.00 | 1.14 | 1,000 | 61 | 85,567 | May 27, 2011 | Final 2011 census result |
| 50 |  Dominica | 71,000 | 0.00 | 0.00 | 0 | - | 71,293 | May 14, 2011 | Preliminary 2011 census result |
| 51 |  Saint Kitts and Nevis | 46,000 | 0.00 | 0.00 | 0 | - | 46,204 | May 15, 2011 | 2011 census result |
| 52 |  Tuvalu | 11,000 | 0.00 | 0.00 | 0 | - | 11,300 | 2015 | Official estimate |
| 53 |  Nauru | 10,000 | 0.00 | 0.00 | 0 | - | 10,900 | 2015 | Official estimate |
| Total | 2,357,512,000 | 100.00 | 1.37 | 42,433,000 | 64 |
In mathematics, the geometric mean is a mean or average which indicates a central tendency of a finite set of real numbers by using the product of their values. The geometric mean is defined as the nth root of the product of n numbers, i.e., for a set of numbers a1, a2, ..., an, the geometric mean is defined as
In mathematics, the logarithm is the inverse function to exponentiation. That means that the logarithm of a number x to the base b is the exponent to which b must be raised to produce x. For example, since 1000 = 103, the logarithm base of 1000 is 3, or log10 (1000) = 3. The logarithm of x to base b is denoted as logb (x), or without parentheses, logb x. When the base is clear from the context or is irrelevant it is sometimes written log x.
In mathematics, the common logarithm is the logarithm with base 10. It is also known as the decadic logarithm and as the decimal logarithm, named after its base, or Briggsian logarithm, after Henry Briggs, an English mathematician who pioneered its use, as well as standard logarithm. Historically, it was known as logarithmus decimalis or logarithmus decadis. It is indicated by log(x), log10(x), or sometimes Log(x) with a capital L; on calculators, it is printed as "log", but mathematicians usually mean natural logarithm (logarithm with base e ≈ 2.71828) rather than common logarithm when writing "log". To mitigate this ambiguity, the ISO 80000 specification recommends that log10(x) should be written lg(x), and loge(x) should be ln(x).
Exponential growth is a process that increases quantity over time at an ever-increasing rate. It occurs when the instantaneous rate of change of a quantity with respect to time is proportional to the quantity itself. Described as a function, a quantity undergoing exponential growth is an exponential function of time, that is, the variable representing time is the exponent. Exponential growth is the inverse of logarithmic growth.
In finance, moneyness is the relative position of the current price of an underlying asset with respect to the strike price of a derivative, most commonly a call option or a put option. Moneyness is firstly a three-fold classification:
The Human Development Index (HDI) is a statistical composite index of life expectancy, education, and per capita income indicators, which is used to rank countries into four tiers of human development. A country scores a higher level of HDI when the lifespan is higher, the education level is higher, and the gross national income GNI (PPP) per capita is higher. It was developed by Pakistani economist Mahbub ul-Haq and was further used to measure a country's development by the United Nations Development Programme (UNDP)'s Human Development Report Office.
In finance, the rule of 72, the rule of 70 and the rule of 69.3 are methods for estimating an investment's doubling time. The rule number is divided by the interest percentage per period to obtain the approximate number of periods required for doubling. Although scientific calculators and spreadsheet programs have functions to find the accurate doubling time, the rules are useful for mental calculations and when only a basic calculator is available.
The Lotka–Volterra equations, also known as the Lotka–Volterra predator–prey model, are a pair of first-order nonlinear differential equations, frequently used to describe the dynamics of biological systems in which two species interact, one as a predator and the other as prey. The populations change through time according to the pair of equations:
Population dynamics is the type of mathematics used to model and study the size and age composition of populations as dynamical systems.
Future value is the value of an asset at a specific date. It measures the nominal future sum of money that a given sum of money is "worth" at a specified time in the future assuming a certain interest rate, or more generally, rate of return; it is the present value multiplied by the accumulation function. The value does not include corrections for inflation or other factors that affect the true value of money in the future. This is used in time value of money calculations.
In probability theory and statistics, the coefficient of variation (CV), also known as normalized root-mean-square deviation (NRMSD), percent RMS, and relative standard deviation (RSD), is a standardized measure of dispersion of a probability distribution or frequency distribution. It is defined as the ratio of the standard deviation to the mean , and often expressed as a percentage ("%RSD"). The CV or RSD is widely used in analytical chemistry to express the precision and repeatability of an assay. It is also commonly used in fields such as engineering or physics when doing quality assurance studies and ANOVA gauge R&R, by economists and investors in economic models, and in psychology/neuroscience.
Pediatric end-stage liver disease (PELD) is a disease severity scoring system for children under 12 years of age. It is calculated from the patient's albumin, bilirubin, and international normalized ratio (INR) together with the patient's age and degree of growth failure. This score is also used by the United Network for Organ Sharing (UNOS) for prioritizing allocation of liver transplants.
Gross tonnage is a nonlinear measure of a ship's overall internal volume. Gross tonnage is different from gross register tonnage. Neither gross tonnage nor gross register tonnage should be confused with measures of mass or weight such as deadweight tonnage or displacement.
The doubling time is the time it takes for a population to double in size/value. It is applied to population growth, inflation, resource extraction, consumption of goods, compound interest, the volume of malignant tumours, and many other things that tend to grow over time. When the relative growth rate is constant, the quantity undergoes exponential growth and has a constant doubling time or period, which can be calculated directly from the growth rate.
The Brain Fuck Scheduler (BFS) is a process scheduler designed for the Linux kernel in August 2009 based on earliest eligible virtual deadline first scheduling (EEVDF), as an alternative to the Completely Fair Scheduler (CFS) and the O(1) scheduler. BFS was created by Con Kolivas.