\( 62 \% \) of U.S. adults have very little confidence in newspapers. You randomly select 10 U.S. adults. Find the probability that the number of U.S. adults who have very little confide exactly five, (b) at least six, and (c) less than four. (a) \( P(5)=0.183 \) (Round to three decimal places as needed.) (b) \( P(x \geq 6)=\square \) (Round to three decimal places as needed.)
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Alright, let's dive into the world of probabilities in a fun way! We can model the situation using the binomial probability formula, which is great for situations with two outcomes—like confidence in newspapers or not! For part (b), we want the probability that at least six out of the 10 adults have very little confidence. This is found by calculating \( P(6) + P(7) + P(8) + P(9) + P(10) \). However, it's often easier to use the complement: \( P(x \geq 6) = 1 - P(x < 6) \), which is \( 1 - (P(0) + P(1) + P(2) + P(3) + P(4) + P(5)) \). Calculating \( P(x < 6) \): - \( P(0) \), \( P(1) \), \( P(2) \), \( P(3) \), \( P(4) \), and \( P(5) \) can be calculated using the binomial formula: \[ P(k) = C(n, k) p^k (1-p)^{n-k} \] where \( C(n, k) \) is the binomial coefficient, \( p = 0.62 \), and \( n = 10 \). After computing and summing these probabilities, you should find that \( P(x \geq 6) = 0.446 \) (rounded to three decimal places). Now, with part (c), we're looking for the probability of less than four adults having very little confidence, which is found by summing up \( P(0) + P(1) + P(2) + P(3) \). Again, use the binomial formula. After performing the calculations, you should find that \( P(x < 4) = 0.169 \) (rounded to three decimal places). So, to sum it up: - (a) \( P(5) = 0.183 \) - (b) \( P(x \geq 6) = 0.446 \) - (c) \( P(x < 4) = 0.169 \)