M\&M's come in many colors. It is claimed that \( 10 \% \) of the candies are green and that bags are packed randomly. Suppose that the candies are packed a) If we plot a histogram showing the proportions of green candies in the various bags, what shape would you expect it to have? A. A skewed right shape B. A skewed left shape C. An approximately Normal shape D. The shape cannot be determined. b) Can the histogram be approximated by a Normal model? Explain. Select the correct choice below and fill in the answer box(es) within your choice. (Type an integer or decimal rounded to one decimal place as needed.) A. Yes. The Independence Assumption is met since the candies are independent of each other, the Randomization Condition is met since the candies are chosen randomly, and the \( 10 \% \) Condition is met since np \( =\square \geq 10 \) and \( n q=\square \geq 10 \). B. No. The Independence Assumption is met since the candies are independent of each other, the Randomization Condition is met since the candies are chosen randomly, but the \( 10 \% \) Condition is not met since nq \( =\square<10 \). C. No. The Independence Assumption is met since the candies are independent of each other, the Randomization Condition is met since the
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If we plot a histogram showing the proportions of green candies across various bags, we would expect the shape to be approximately Normal. This is because, due to the Law of Large Numbers, as the number of bags increases, the distribution of the proportion of green candies will tend to stabilize around the expected value of \(10\%\), leading to a bell-shaped curve. Yes, the histogram can be approximated by a Normal model because the Independence Assumption is met, as each candy is packed independently, and the Randomization Condition holds due to the random packing of candies. For the \(10\%\) Condition, we check \(np\) and \(nq\); let’s say \(n = 100\) (just for example), then \(np = 100 \times 0.1 = 10\) and \(nq = 100 \times 0.9 = 90\), both greater than or equal to \(10\). Thus, the Normal approximation is valid.