STAT 200 Lecture Notes - Sampling Distribution, Standard Deviation, Central Limit Theorem
Document Summary
Standard error- standard deviation of sampling distributions. Central limit theorem- states that if a large enough sample is taken (typically n > 30) then the sampling distribution of x bar is approximately a normal distribution with a mean of and a standard deviation of. The law of large numbers- as the sample size increases the sample mean will approach the population mean. The central limit theorem- as the sample size increases the sampling distribution of x-bar approaches the normal distribution. The central limit theorem- important because it enables us to calculate probabilities about sample means. Text chapter 7: how sample proportions vary around the population proportion. Sampling distribution- helps us determine how close to the population parameter a sample statistic is likely to fall. Shows all possible values of the sample proportion and how often these sample proportions are expected to occur in random sampling. Parameter- a numerical summary of a population.
