[Sampling] Population Proportion (Non-replacement Sampling from a Finite Population)
Author: RumexJaponicusExecutions: 1600
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In a non-replacement sample from a finite population, this function calculates the “population proportion” that satisfies the specified conditions.
If a non-replacement sampling of “sample size” is repeated infinitely many times from a population with a “population size” and a “population proportion,” the “sample proportion” will, with the probability specified by the “confidence level,” approximately satisfy the following condition:
| Sample proportion - Population proportion | ≤ Margin of error
| Sample proportion - Population proportion | ≤ Margin of error
| Population ratio 1 | |
| Population Ratio 2 | |
| Error Code | |
| Estimate of the population proportion of 1 | |
| Estimated value with a margin of error of 1 | |
| Estimated value with a confidence level of 1 | |
| Estimated value of the population proportion 2 | |
| Estimated value with a margin of error of 2 | |
| Estimated value with a confidence level of 2 |
[Error Codes]
Error Code Description
0 Exited normally.
101 Does not satisfy (0 < population proportion - margin of error ∧ population proportion + margin of error < 1).
102 Does not satisfy (1 / population size ≤ population proportion ≤ 1 - 1 / population size).
103 Does not satisfy (0 < population proportion < 1).
104 ( Population proportion ∈ R ) is not true.
201 ( Sample size < Population size ) is not true.
301 ( 2 ≤ sample size ∧ sample size ∈ N ) is not true.
302 ( 0 < confidence level < 1 ) is not true.
303 ( 0 < margin of error < 0.5 ) is not true.
304 ( 3 ≤ population size ∧ population size ∈ N ) is not true.
* N is the set of all natural numbers
* R is the set of all real numbers
[Evaluation of Calculation Results]
If the “Error Code” is 0, the calculation results are evaluated based on the hypergeometric distribution. In this evaluation, optimal values are estimated in the order of “population proportion,” “margin of error,” and “confidence level.”
Estimated population proportion P
Estimated margin of error e
Estimated confidence level A
Where
P = M / population size
M = [population size * population proportion + 0.5]
e = max(|e_min|, |e_max|)
e_min = m_min / sample size - P
e_max = m_max / sample size - P
m_min = [sample size * (P - margin of error) + 0.5]
m_max = [sample size * (P + margin of error) + 0.5]
A = 1 - (B_lower + B_upper)
Sample size
B_lower = 1 - Σ H(x; population size, sample size, P)
x = m_min
m_max
B_upper = 1 - Σ H(x; population size, sample size, P)
x = 0
where H(x; N, n, p) is the probability mass function of the hypergeometric distribution.
Error Code Description
0 Exited normally.
101 Does not satisfy (0 < population proportion - margin of error ∧ population proportion + margin of error < 1).
102 Does not satisfy (1 / population size ≤ population proportion ≤ 1 - 1 / population size).
103 Does not satisfy (0 < population proportion < 1).
104 ( Population proportion ∈ R ) is not true.
201 ( Sample size < Population size ) is not true.
301 ( 2 ≤ sample size ∧ sample size ∈ N ) is not true.
302 ( 0 < confidence level < 1 ) is not true.
303 ( 0 < margin of error < 0.5 ) is not true.
304 ( 3 ≤ population size ∧ population size ∈ N ) is not true.
* N is the set of all natural numbers
* R is the set of all real numbers
[Evaluation of Calculation Results]
If the “Error Code” is 0, the calculation results are evaluated based on the hypergeometric distribution. In this evaluation, optimal values are estimated in the order of “population proportion,” “margin of error,” and “confidence level.”
Estimated population proportion P
Estimated margin of error e
Estimated confidence level A
Where
P = M / population size
M = [population size * population proportion + 0.5]
e = max(|e_min|, |e_max|)
e_min = m_min / sample size - P
e_max = m_max / sample size - P
m_min = [sample size * (P - margin of error) + 0.5]
m_max = [sample size * (P + margin of error) + 0.5]
A = 1 - (B_lower + B_upper)
Sample size
B_lower = 1 - Σ H(x; population size, sample size, P)
x = m_min
m_max
B_upper = 1 - Σ H(x; population size, sample size, P)
x = 0
where H(x; N, n, p) is the probability mass function of the hypergeometric distribution.
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