STAT151 Lecture Notes - Lecture 23: Central Limit Theorem, Confidence Interval, Probability Plot

• School: University of Alberta
• Department: Statistics
• Course: STAT151
• Professor: Omar Rivasplata

Document Summary

We learned how to create conidence intervals and test hypotheses about proporions. It"d be nice to be able to do the same for means. Just as we did before, we will base both our conidence interval and our hypothesis test on the sampling distribuion model. The central limit theorem told us that the sampling distribuion model for means is normal with mean and standard deviaion. All we need is a random sample of quanitaive data. And the true populaion standard deviaion, : well, that"s a problem . Proporions have a link between the proporion value and the standard deviaion of the sample proporion. This is not the case with means knowing the sample mean tells us nothing about sd ( y ) We"ll do the best we can: esimate the populaion parameter with the sample staisic s. Our resuling standard error is sd ( y )= s.