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Bootstrapping: A Nonparametric Approach to Statistical Inference - Quantitative Applications in the Social Sciences Christopher Z. Mooney
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Bootstrapping: A Nonparametric Approach to Statistical Inference - Quantitative Applications in the Social Sciences
Christopher Z. Mooney
Bootstrapping, a computational nonparametric technique for "re-sampling," enables researchers to draw a conclusion about the characteristics of a population strictly from the existing sample rather than by making parametric assumptions about the estimator. Using real data examples from per capita personal income to median preference differences between legislative committee members and the entire legislature, Mooney and Duval discuss how to apply bootstrapping when the underlying sampling distribution of the statistics cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, they show the advantages and limitations of four bootstrap confidence interval methods: normal approximation, percenti
80 pages, illustrations
| Media | Books Paperback Book (Book with soft cover and glued back) |
| Released | September 29, 1993 |
| ISBN13 | 9780803953819 |
| Publishers | SAGE Publications Inc |
| Pages | 80 |
| Dimensions | 137 × 216 × 4 mm · 100 g |
| Language | English |
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