01:960:211 Study Guide - Consistent Estimator, Bias Of An Estimator, Central Limit Theorem

• School: Rutgers University
• Department: Statistics
• Course: 01:960:211
• Professor: All

Document Summary

After completing this chapter, you should be able to: _ sampling distribution of the sample mean, x. Determine the mean and standard deviation for the. _ sampling distribution of the sample proportion, p. Describe the central limit theorem and its importance. Apply sampling distributions for both x and p. Population parameters ex: x is an estimate of the population mean, . Different samples provide different estimates of the population parameter. Sample results have potential variability, thus sampling error exits. The difference between a value (a statistic) computed from a sample and the corresponding value (a parameter) computed from a population. = population mean x = sample mean xi = values in the population or sample. N = population size n = sample size. If the population mean is = 98. 6 degrees and a sample of n = 5 temperatures yields a sample mean of = 99. 2 degrees, then the sampling error is. Different samples will yield different sampling errors.