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The only information we have is the percentage of satisfied customers from our prior work history, so we use that to calculate our best predicted probability of getting a satisfied client. With 22 out of 25 satisfied clients, that gives us: p = P (success) = 22/25 = 0.88 𝑘−1 We use the best predicted probability of success we found on the last slide as our probability of success in a binomial distribution. We will have 60 clients, so that is 60 trials. Getting at least 85% satisfied customers means getting at least 40 success, so we will use: p = 0.88 k= 51 n = 60 𝑃 𝑥 ≥𝑘 =1−෍ 𝑖=0 𝑛 𝑖 𝑝 1−𝑝 𝑖 𝑛−𝑖 50 𝑃 𝑥 ≥ 51 = 1 − ෍ 𝑖=0 60 0.88𝑖 0.12 𝑖 60−𝑖 𝑃 𝑥 ≥ 51 = 0.8224 We find an 82% chance of getting at least 51 out of 60 satisfied customers. Since we have an 82% chance of getting at least 51 out of 60 satisfied customers, G&B Consulting should feel confident that we will hit the mark to maintain our BBB rating. When we fix the Competitor to Don’t Sue, the Manufacturer’s preference is again (24 > -12). Since the Manufacturer always prefers Sue no matter what the Competitor does, Sue is a dominant strategy for the Manufacturer. Now we switch roles and do the same thing for the competitor. When we fix the Manufacturer to Sue, the Competitor’s preference is Sue (-6> -24). When we fix the Manufacturer to Don’t Sue, the Competitor’s preference is again Sue (18> 12). Since the Competito ...
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