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Week 7 Study Notes

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University of Toronto St. George
Damian Dupuy

GGR 270 Lecture 7 October 27, 2010 Central Limit Theorem II The frequency of sample means will be normally distributed When the sample size is large, the sample mean is likely to be quite close to the population mean A large sample is more likely to be closer to the true population mean than a smaller sample Variability Standard deviation of the sampling distribution is equal to the sample standard deviation divided by the square root of the sample size This is called the standard error of the mean o Indicates how much a typical sample mean is likely to differ from the true population mean o Measures the amount of sampling error o The larger the sample size (n), the smaller the amount of sampling error How large is large If sampled population is normal, then sampling distribution of means will also be normal, no matter what the sample size If the sampled population is approximately normal, then the sampling distribution of means will be approximately normal for relatively small sample sizes When the population is skewed, the sample size must be large (n>30) before the sampling distribution will become normal Sample Estimation
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