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Parametric and non-parametric tests
Abstract Parametric and non-parametric methods for analysing continuous data are described in this chapter.Phil Ambery +2 more
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Parametric Testing of Optical Interfaces
2008 IEEE International Test Conference, 2008This series will discuss board and system level testing of high speed optical interfaces. Today these interfaces operate up to 10 Gbps over short, medium and long ranges. In the not too distant future, these interfaces will be operating in ranges up to 40 Gbps.
Brice Achkir, Pavel Zivny, Bill Eklow
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A comparison of parametric and non-parametric statistical tests
BMJ, 2015Researchers investigated the effectiveness of corticosteroids in reducing respiratory disorders in infants born at 34-36 weeks’ gestation. A randomised placebo controlled trial was performed. The intervention was treatment with betamethasone, 12 mg intramuscularly daily for two consecutive days at 34-36 weeks of pregnancy.
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Parametric Tests of Hypotheses
2003In statistical data analysis an important objective is the capability of making decisions about population distributions and statistics based on samples. In order to make such decisions, a hypothesis is formulated, e.g. “is one manufacture method better than another?”, and tested using an appropriate methodology.
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1980
The corpus of theory on non-parametric approaches to outlier testing is not large, and given the strongly parametric nature of the outlier model, this is really not surprising. Most of the results that there are relate to slippage rather than outlier problems.
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The corpus of theory on non-parametric approaches to outlier testing is not large, and given the strongly parametric nature of the outlier model, this is really not surprising. Most of the results that there are relate to slippage rather than outlier problems.
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On Parametric Hypothesis Testing with Non-Parametric Tests
Theory of Probability & Its Applications, 1970openaire +1 more source
2000
Parametric tests require some specific conditions about the distributions of scores in the populations of interest. When these conditions cannot be formally tested, researchers assume that they exist. The interpretation of the results derived from parametric tests relies heavily on these requirements not being seriously violated. When these assumptions
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Parametric tests require some specific conditions about the distributions of scores in the populations of interest. When these conditions cannot be formally tested, researchers assume that they exist. The interpretation of the results derived from parametric tests relies heavily on these requirements not being seriously violated. When these assumptions
openaire +1 more source