MGMT 1050 Chapter Notes - Chapter 9: Sampling Distribution, Standard Deviation, Sampling Error
Document Summary
Sample distribution of the mean of two dice. All possibilities from two dices, and mean of two dices. P(x) is uniform, while the distribution of the mean looks likes a normal curve. Variance needs to be adjusted in a sample size. Standard dev needs to be adjusted in a sample size. Central limit theorem: the sampling distribution of the mean of a random sample drawn from any population is approximately normal for a sufficiently large sample size. The larger the sample size, the more closely the sampling distribution of the mean will resemble a normal distribution. Accuracy depends on the probability distribution of the population and on the sample size. If the population is normal, the mean is normally distributed for all values of n. If the population is nonnormal, the mean is approximately normal only for larger values of n. Mean of the sampling distribution is always equal to the mean of the population.
