STAT200 Lecture Notes - Lecture 5: Sampling Distribution, Bias Of An Estimator, Statistic
4 Aug 2018
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Document Summary
Sampling distribution of the mean, key to the logic of inference. It is based on the notion of taking many, many samples and making an estimate from each sample: sampling error , parameter numerical descriptive measure of the population, we use greek terms to represent it. We can think of this as homogeneity: the larger the sample size, the more homogenous the population, the, properties of the sampling distribution for the mean smaller the standard error is for our estimator. If a random sample of size n is drawn from a population with the mean my and. Sampling distributions, hypothesis tests and confidence intervals: a major theorem is the central limit theorem, we use two theorems to help us make inferences. If repeated samples of a variable y of the size n are drawn rom a normal distribution, with the mean and variance: the sampling distribution of the mean will be a normal distribution with, mean, variance.
