QMS 102 Lecture Notes - Lecture 9: Central Limit Theorem, Bias Of An Estimator, Normal Distribution

• School: Ryerson University
• Department: Quantitative Methods
• Course: QMS 102
• Professor: Gunawardena Egodawatte

Document Summary

In this lesson, you will learn about sampling distributions, and the central limit. There are three good reasons for selecting a sample over selecting the entire population: it is less time-consuming, it is cost effective, it is less cumbersome. To decide whether a production process is producing standard items, we take information from a sample of items and calculate a related statistic (sample mean or sample proportion). To obtain an estimate of the votes that a particular candidate would obtain in a poll, we again take information from a sample of voters and calculate the sample proportion. In both of the above examples, we are going to get an estimation of a population parameter by using a sample statistics. Each sample (second, third, fourth etc. ) we draw will not give us the same result. Therefore, the estimated population parameters also will have different values. This is problematic and to avoid this situation, we use sampling distributions.