QMS 202 Chapter Notes - Chapter 10: Central Limit Theorem, Confidence Interval, New Zealand

• School: Ryerson University
• Department: Quantitative Methods
• Course: QMS 202
• Professor: Daniel Mc Cleary

Document Summary

Confidence interval estimation for the mean (o unknown) Use central limit theorem and knowledge of the population distribution to determine the percentage of sample means that are within certain distances of the population mean. The fact that sample statistics vary from sample to sample is called sampling error: sampling error is the variation that occurs due to selecting a single sample from the population. Level of confidence - is symbolized by (1 - a) x 100% where a is the proportion in the upper tail of the distribution is a/2, and the proportion in the lower tail of the distribution is a/2. X + z a / 2 (o / n(cid:524) X - z a / 2 (o / n(cid:524) < u < x + z a / 2 (o / n(cid:524) Where z a / 2 is the value corresponding to an upper-tail probability of a/2 from the standardized normal distribution.