QMB-210 Lecture Notes - Lecture 17: Standard Deviation, South Dakota Highway 10, Central Limit Theorem

• School: University of Tampa
• Department: Quantitative Methods in Business
• Course: QMB-210
• Professor: Shaista Nisar

Document Summary

Chapter 7: examining the entire population would be expensive and time consuming, ca(cid:374)"t e(cid:454)a(cid:373)i(cid:374)e e(cid:448)er(cid:455)thi(cid:374)g if the test is destru(cid:272)ti(cid:448)e. If a sample is selected properly and the analysis performed correctly, sample information can be used to make an accurate assessment of the entire population. Continents, states, counties, countries: resampling, nonprobability sampling, convenience, make a sample based on your convenience, e. g. the first people you see when you start your sample. According to the central limit theorem, sample means from samples of sufficient size, drawn from any population, will be normally distributed. In most cases, sample sizes of 30 or larger will result in sample means being normally distributed, regardless of the shape of the population distribution. If the population follows the normal probability distribution, the sample means will also be normally distributed, regardless of the size of the samples. Formula for the z-score for the sample mean.