Probability of a Confidence Interval

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SUMMARY

The probability that at least 85 out of 100 predicted means will fall within the calculated confidence interval (x̄ ± 1.645(σ/√n)) at an alpha level of 10% is approximately 96%. This conclusion is derived from the application of the binomial distribution, specifically using the formula sum(n=85,100,0.9^n*0.1^(100-n)*binomial(100,n)). The Central Limit Theorem supports the assumption of normality due to the sample size of 100, which is greater than 30.

PREREQUISITES
  • Understanding of confidence intervals and their calculation
  • Familiarity with the Central Limit Theorem
  • Knowledge of binomial distribution and its applications
  • Basic statistical concepts such as mean and standard deviation
NEXT STEPS
  • Study the derivation and application of confidence intervals in statistics
  • Learn about the Central Limit Theorem and its implications for sample sizes
  • Explore binomial distribution calculations and their relevance in hypothesis testing
  • Investigate advanced statistical software tools for performing these calculations, such as R or Python's SciPy library
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Statisticians, data analysts, researchers, and students who are involved in statistical analysis and interpretation of confidence intervals.

shalomhk
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What's the probability that a confidence interval (with alpha=10%), will have at least 85 of 100 predicted means within the calculated interval range (xbar +/- 1.645(sigma/sqrt(n)))?

I understand that on average 90% of my means will be located in this range (and I know how to find this range), but the figure 90% is an AVERAGE.

Suppose I do this experiment ONCE, and only once. What's the probability that at least (>=) 85 (of the 100, or 85%) of the mean values will be within this range?

Assumptions: Normal (by Central Limit Theorem as n=100 is greater than 30)


Thanks!
 
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sum(n=85,100,.9^n*.1^(100-n)*binomial(100,n)) ≈ 96% of the time.
 
Thanks!
 

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