STA 309 Chapter Notes - Chapter 9: Central Limit Theorem, Sampling Distribution, Standard Deviation
Document Summary
Chapter 9: sampling distributions and confidence intervals for proportions. Sampling distribution: the distribution of the sample proportion. Central limit theorem (clt): theorem that says the normal model works. The distribution of independent random samples gets closer to a normal model as the sample size grows. Independence assumption: sampled values must be independent of each other. Sample size assumption: the sample size, n, must be large enough. Randomization condition: make sure the sampling method was not biased and data are representative of the population. 10% condition: if the sample is too large a fraction of the population, the independence assumption won"t be satisfied. Success/failure condition: must expect at least 10 successes and at least 10 failures. Standard error (se): estimate of standard deviation of a sampling distribution. Format: we are ___% confident that between __% and __% of. The extent of the interval on either side of p .
