STA220H1 Lecture Notes - Lecture 9: Central Limit Theorem, Bias Of An Estimator, Sampling Distribution
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Estimator: a function of sample observations used to estimate a population parameter. A probability distribution: function, table or graph that determines the probabilities of all values of a random variable. A normal distribution: specified in terms of its mean and standard deviation. An unbiased estimator: has its expectation equal to the parameter. A sampling distribution: probability distribution of an estimator. Central limit theorem: in repeated sampling, the probabilities for mean results. Sampling distribution of the sample mean or the proportions of successes converge to a normal distribution. Suppose (cid:2869),(cid:2870),,are independent random variables with common probability distribution such that. Is also a random variable, so it has a probability distribution. The probability distribution of the sample mean is called the sampling distribution of the sample mean. Set skew = 0 and number of trials = 100. As sample size increases, how does the distribution of observed sample means.
