Question 1 Given the number of trials and the probability of success, determine the probability indicated: \( \mathrm{n}=12, \mathrm{p}=0.7 \), find \( \mathrm{P}(7 \) successes) \( \mathrm{P}(7 \) successes \( )= \) Submit Question
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Beyond the Answer
To find the probability of getting exactly 7 successes out of 12 trials with a success probability of 0.7, we can use the binomial probability formula: \[ P(X = k) = \binom{n}{k} p^k (1-p)^{n-k} \] Plugging in the values: \[ P(7) = \binom{12}{7} (0.7)^7 (0.3)^{5} \] The calculation for \( \binom{12}{7} \) gives us 792. Continuing with that, calculate \( (0.7)^7 \) and \( (0.3)^5 \): Now, \( P(7) = 792 \times (0.7)^7 \times (0.3)^5 \). After crunching the numbers, you'll find \( P(7) \) is approximately 0.1935. So, \( P(7 \text{ successes}) \approx 0.1935 \). The binomial distribution is not just numbers; it's a tool used by statisticians and data analysts to predict outcomes based on past data! It's like rolling a die with a twist: you can predict how likely an event is to happen over multiple trials. Whether it's for quality control in manufacturing or understanding customer behavior, it's all about estimating the chances! Imagine you’re planning a party and expecting 12 guests, but you're not sure how many will actually show up. If you know that on average, 70% of your friends RSVP "yes," you can apply the binomial distribution to get a sense of how many to expect. It can help you decide on the right amount of pizza, drinks, and party favors to get—all while keeping calculations fun and a bit suspenseful!
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