Final answer:
To estimate the minimum sample size needed to estimate the population proportion with 90% confidence and an error margin of 3%, we can use the formula: n = (Z^2 * p * (1 - p)) / E^2. Since we don't have a preliminary estimate for the population proportion, we assume the worst-case scenario where p = 0.5, which gives us the largest possible sample size: n = (1.645^2 * 0.5 * (1 - 0.5)) / 0.03^2 = 752.852 ≈ 753. Therefore, the minimum sample size needed to estimate the population proportion with 90% confidence and an error margin of 3% is 753.
Step-by-step explanation:
To estimate the minimum sample size needed to estimate the population proportion with 90% confidence and an error margin of 3%, we can use the formula:
n = (Z^2 * p * (1 - p)) / E^2
Where:
- n is the sample size
- Z is the Z-score for the desired confidence level (1.645 for 90% confidence)
- p is the hypothesized population proportion (we don't have it in this case)
- E is the desired error margin (0.03)
Since we don't have a preliminary estimate for the population proportion, we assume the worst-case scenario where p = 0.5, which gives us the largest possible sample size:
n = (1.645^2 * 0.5 * (1 - 0.5)) / 0.03^2 = 752.852 ≈ 753
Therefore, the minimum sample size needed to estimate the population proportion with 90% confidence and an error margin of 3% is 753.