STAT151 Lecture Notes - Lecture 18: Simple Random Sample, Sampling Distribution, Central Limit Theorem

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

Document Summary

Sampling distribuion model: diferent random samples give diferent values for a staisic; the sampling distribuion model shows the behavior of the staisic over all the possible samples for the same size n. To use a normal model, we need to specify two parameters: its mean and standard deviaion. When it comes to proporions, if we have the mean, , we automaically also know the standard deviaion for ^p , the proporion of successes: ( ^p)= sd ( ^p )= p ( 1 p) n. When we draw simple random samples of n individuals, the proporions we ind will vary from sample to sample o. As long as n is reasonably large, we can model the distribuion of these sample proporions will a probability model that is: Sampling error/variability: the variability we expect to see from one random sample to another; it is someimes called sampling error, but sampling variability is the beter term.