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Lecture 6

ECON 2500 Lecture 6: Chapter 16 Bootstrap Methods and Permutation Tests Lecture 6
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Department
Economics
Course
ECON 2500
Professor
Andrei Semenov
Semester
Fall

Description
Chapter 16 Bootstrap Methods and Permutation Tests Lecture 6 • • Spread: The bootstrap standard error of a statistic is the standard deviation of its boots trap distribution. • The boots trap standard foretime test he standard deviation of the sampling distribution of the statistic. • Bootstrap t confidence intervals If the boots trap distribution of astatistics how sa Normal shape and small bias, we can get a confidence interval for the parameter by using the bootstrap standard rora nd the familiar distribution. An example will show how this works. EXAMPLE • Selling prices of residential real estate. We are interested in the selling prices of residential real estate in Seattle, Washington. • Table 16.1 displays the selling prices of a random sample of 50 pieces of real estate sold in Seattle during 2002, as recorded by the county assessor.6 Unfortunately, the data do not distinguish residential property from commercial property. • Most sales are residential, but a few large commercial sales in a sample can greatly increase the sample mean selling price. Figure16.6 shows the distribution of the sample prices. • The distribution is far from Normal, with a few high outliers that may be commercial sales. The sample is small, and the distribution is highly skewed and “contaminated” by an unknown number of commercial sales. • How can we estimate the center of the distribution despite these difficulties? • The first step is to abandon the mean as a measure of center in favor of a statistic that is more resistant to outliers. • We might choose the median, but in this case we will use the 25% trimmed mean, the mean of them idle 50% of the trimmed mean, page 53 observations. The median is the middle or mean of the 2 middle observations. • The trimmed mean of tendoes a better job of representing the average of typical observations
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