STAT 2000 Lecture 14: February 20

• School: University of Georgia
• Department: Statistics
• Course: STAT 2000
• Professor: Dunlap

Document Summary

7. 1 how sample proportions vary around the population proportion. Simulation: using a computer to draw random samples many times same population. Statistics have sampling distributions: ,, : (cid:4666) (cid:4667)= (cid:3043) (cid:3044) (cid:3043)(cid:4666)(cid:2869) (cid:3043)(cid:4667, ~(cid:4666) (cid:3043)(cid:3044) ) Sampling distribution: distribution of proportion over many independent samples from the. Distributions that are bell shaped and centered at the true proportion (p)- we can use sample size (n) to find the standard deviation of the sampling distribution (standard error) Independence assumption: sampled values must be independent of each other: randomization condition: subjects should be randomly assigned treatments, sample should be a simple random sample or another unbiased sampling method. Larger sample sizes=less deviation/variability; will be more bell shaped. Example 1: bank loans n=300 mean=0. 07: mean of sampling distribution of =(cid:882). (cid:882)7, std. error of = (cid:4666)(cid:2868). (cid:2868)7(cid:4667) (cid:4666)(cid:2868). 93(cid:4667)