You may need to use the appropriate appendix table or technology to answer this question. Assume a binomial probability distribution has \( p=0.60 \) and \( n=300 \). (a) What are the mean and standard deviation? (Round your answers to two decimal places.) mean \( \square \) standard deviation \( \square \) (b) Is this situation one in which binomial probabilities can be approximated by the normal probability distribution? Explain. Yes, because \( n p \geq 5 \) and \( n(1-p) \geq 5 \), Yes, because \( n p<5 \) and \( n(1-p)<5 \), \( \mathrm{No}_{\text {, because }} n p \leqslant 5 \) and \( n(1-p) \leqslant 5_{1} \) \( \mathrm{No}_{1} \) because \( n p \geq 5 \) and \( n(1-p) \geq 5{ }_{1} \) Yes, because \( n \geqslant 30 \). (c) What is the probability of 160 to 170 successes? Use the normal approximation of the binomial distribution to answer this question. (Round your answer to four decimal places.) \( \square \) (d) What is the probability of 190 or more successes? Use the normal approximation of the binomial distribution to answer this question. (Round your answer to four decimal places.) \( \square \) (e) What is the advantage of using the normal probability distribution to approximate the binomial probabilities? The advantage would be that using the normal probability distribution to approximate the binomial probabilities increases the number of calculations. The advantage would be that using the normal probability distribution to approximate the binomial probabilities reduces the number of calculations. The advantage would be that using the normal probability distribution to approximate the binomial probabilities makes the calculations more accurate. The advantage would be that using the normal probability distribution to approximate the binomial probabilities makes the calculations less accurate. How would you calculate the probability in part (d) using the binomial distribution. (Use \( f(x) \) to denote the binomial probability function.) \( P(x \geq 190)=1-f(189)-f(190)-f(191)-f(192)-\ldots=f(300) \) \( P(x \geq 190)=f(0)+f(1)+\ldots+f(189)+f(190) \) \( P(x \geq 190)=f(191)+f(192)+f(193)+f(194)+\ldots+f(300) \) \( P(x \geq 190)=f(0)+f(1)+\ldots+f(188)+f(189) \) \( P(x \geq 190)=f(190)+f(191)+f(192)+f(193)+\ldots+f(300) \)
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To find the mean and standard deviation of a binomial distribution, you can use the formulas: Mean \( \mu = np \) and Standard Deviation \( \sigma = \sqrt{np(1-p)} \). Here \( n = 300 \) and \( p = 0.60 \). Thus, the mean is \( \mu = 300 \times 0.60 = 180 \) and the standard deviation is \( \sigma = \sqrt{300 \times 0.60 \times 0.40} \approx 8.75 \). When evaluating the normal approximation for this binomial distribution, we check two conditions: \( np = 180 \) and \( n(1-p) = 120 \). Both are greater than 5, which means yes, we can safely use the normal distribution to approximate the binomial probabilities, allowing us to simplify calculations. The probability of 160 to 170 successes can be computed using the cumulative normal distribution and applying continuity correction (adjusting boundaries). You convert \( x=160 \) to \( z \) score, find \( P(159.5 < x < 170.5) \), calculate the z-scores and then use the standard normal table for probabilities. The big win in using the normal approximation is all about efficiency! Instead of figuring out probabilities for each possible outcome in the binomial setup, which could take forever for larger \( n \), the normal curve lets you use z-scores and a table to quickly find the answers. It’s like using a magic shortcut instead of counting sheep!
