Step 1 :The sample size needed for a proportion estimate can be calculated using the formula: \(n = \frac{{Z^2 \cdot p \cdot (1-p)}}{{E^2}}\), where \(n\) is the sample size, \(Z\) is the z-score, \(p\) is the estimated proportion, and \(E\) is the margin of error.
Step 2 :For part (a), we are given a previous estimate of \(p = 0.56\), a margin of error \(E = 0.04\), and a confidence level of 99%, which corresponds to a z-score \(Z = 2.575\).
Step 3 :For part (b), if no prior estimate is given, we use \(p = 0.5\) as this maximizes the product \(p \cdot (1-p)\) and thus gives the largest possible sample size.
Step 4 :Let's calculate the sample size for each part.
Step 5 :For part (a), substituting the given values into the formula, we get \(n_a = \frac{{(2.575)^2 \cdot 0.56 \cdot (1-0.56)}}{{(0.04)^2}} = 1022\).
Step 6 :For part (b), substituting the given values into the formula, we get \(n_b = \frac{{(2.575)^2 \cdot 0.5 \cdot (1-0.5)}}{{(0.04)^2}} = 1037\).
Step 7 :Final Answer: (a) The required sample size when using a previous estimate of 0.56 is \(\boxed{1022}\). (b) The required sample size when not using any prior estimates is \(\boxed{1037}\).