ECON10005 Chapter Notes - Chapter 3: Central Limit Theorem, Variance, Bias Of An Estimator

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Statistical inference is a collection of procedures designed so that data in a sample can be informative in a rigorous way about a particular population. A sample is randomly drawn from a population in a manner designed to provide a representative sample. (i. e. a sample that provides "reliable inference" about the population) Sample: x, (cid:1871)2 (estimators of population parameter) Every individual in the population has an equal probability of being in the sample. Every individual is chosen (or not chosen) independently of every other individual. Suppose 1, 2, (cid:1866) take only values 0 or 1 ( = 1) = (cid:1868) The number of 1"s in the sample is (cid:1866) 1. The sample proportion or sample mean is: Variance of the sample mean (n: sample size) population mean. Different samples -> different x (i. e: x is a random variable) Increasing n -> better x because it evaluates more precisely relative to the.

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