Step 1 :Define the population as [2, 3, 10] and the possible samples of size 2 as [(2, 2), (2, 3), (2, 10), (3, 2), (3, 3), (3, 10), (10, 2), (10, 3), (10, 10)].
Step 2 :Calculate the proportion of odd numbers in each sample. The proportions are [0.0, 0.5, 0.0, 0.5, 1.0, 0.5, 0.0, 0.5, 0.0].
Step 3 :Calculate the probability of each proportion. The probabilities are [0.4444444444444444, 0.4444444444444444, 0.4444444444444444, 0.4444444444444444, 0.1111111111111111, 0.4444444444444444, 0.4444444444444444, 0.4444444444444444, 0.4444444444444444].
Step 4 :Construct the probability distribution table as follows: \[ \begin{array}{ccc} \text{Sample} & \text{Proportion of Odd Numbers} & \text{Probability} \\ \hline (2, 2) & 0.0 & 0.444444 \\ (2, 3) & 0.5 & 0.444444 \\ (2, 10) & 0.0 & 0.444444 \\ (3, 2) & 0.5 & 0.444444 \\ (3, 3) & 1.0 & 0.111111 \\ (3, 10) & 0.5 & 0.444444 \\ (10, 2) & 0.0 & 0.444444 \\ (10, 3) & 0.5 & 0.444444 \\ (10, 10) & 0.0 & 0.444444 \\ \end{array} \]
Step 5 :Calculate the mean of the sample proportions, which is 0.3333333333333333.
Step 6 :Calculate the proportion of odd numbers in the population, which is 0.3333333333333333.
Step 7 :Compare the mean of the sample proportions to the population proportion. They are equal, indicating that the sample proportions target the value of the population proportion.
Step 8 :Conclude that the sample proportion makes a good estimator of the population proportion. \(\boxed{\text{Yes, the mean of the sample proportions equals the proportion of odd numbers in the population. Yes, the sample proportions target the value of the population proportion. Yes, the sample proportion makes a good estimator of the population proportion.}}\)