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- https://stats.libretexts.org/Courses/Fresno_City_College/Book%3A_Business_Statistics_Customized_(OpenStax)/08%3A_Confidence_Intervals/8.11%3A_Chapter_ReviewIn that case, solve the relevant confidence interval formula for n to discover the size of the sample that is needed to achieve this goal: \(n=\frac{Z_{\alpha}^{2} \sigma^{2}}{(\overline{x}-\mu)^{2}}\...In that case, solve the relevant confidence interval formula for n to discover the size of the sample that is needed to achieve this goal: \(n=\frac{Z_{\alpha}^{2} \sigma^{2}}{(\overline{x}-\mu)^{2}}\) If the random variable is binary then the formula for the appropriate sample size to maintain a particular level of confidence with a specific tolerance level is given by \(n=\frac{Z_{\alpha}^{2} \mathrm{pq}}{e^{2}}\)
- https://stats.libretexts.org/Courses/Saint_Mary's_College_Notre_Dame/BFE_1201_Statistical_Methods_for_Finance_(Kuter)/06%3A_Interval_Estimates/6.10%3A_Chapter_ReviewThe confidence interval under this distribution is calculated with \(\overline{x} \pm\left(t_{\frac{\alpha}{2}}\right) \frac{s}{\sqrt{n}}\) where \(t_{\frac{\alpha}{2}}\) is the t-score with area to t...The confidence interval under this distribution is calculated with \(\overline{x} \pm\left(t_{\frac{\alpha}{2}}\right) \frac{s}{\sqrt{n}}\) where \(t_{\frac{\alpha}{2}}\) is the t-score with area to the right equal to \(\frac{\alpha}{2}\), \(s\) is the sample standard deviation, and \(n\) is the sample size. If the random variable is binary then the formula for the appropriate sample size to maintain a particular level of confidence with a specific tolerance level is given by
- https://stats.libretexts.org/Courses/Fresno_City_College/Introduction_to_Business_Statistics_-_OER_-_Spring_2023/08%3A_Confidence_Intervals/8.07%3A_Chapter_ReviewIn that case, solve the relevant confidence interval formula for n to discover the size of the sample that is needed to achieve this goal: \(n=\frac{Z_{\alpha}^{2} \sigma^{2}}{(\overline{x}-\mu)^{2}}\...In that case, solve the relevant confidence interval formula for n to discover the size of the sample that is needed to achieve this goal: \(n=\frac{Z_{\alpha}^{2} \sigma^{2}}{(\overline{x}-\mu)^{2}}\) If the random variable is binary then the formula for the appropriate sample size to maintain a particular level of confidence with a specific tolerance level is given by \(n=\frac{Z_{\alpha}^{2} \mathrm{pq}}{e^{2}}\)
