International cooperation and technology transfer

Fras, Mojca Kosmatin

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Monograph

Persistent identifier:: 856490555

Author:: Fras, Mojca Kosmatin

Title:: International cooperation and technology transfer

Sub title:: Ljubljana, Slovenia, February 2 - 5, 2000 : proceedings of the workshop

Scope:: VI, 163 Seiten

Year of publication:: 2000

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856490555

Illustration:: Illustrationen, Diagramme

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: GPS SURVEYING IN CARTOGRAPHY CERTIFICATION. Vincenzo Barrile, Giovanni Pirrone, Rossella Nocera

Document type:: Monograph

Structure type:: Chapter

Therefore the classical significance tests should be used, when the, a priori, hypotheses on the behaviour of statistical populations are reasonably satisfied. On the contrary when these, a priori, hypotheses cannot be met, hence distribution-free significance tests must be used. There are several situations that require for their use: ♦ ###departure from the normality of population distributions; ♦ ###interdependence among the samples and/or within one or more samples; ♦ ###inequality of dispersion (e.g., variances) of the samples; and, last but not least, samples too small, for the hypotheses on the behaviour of statistical populations could be verified. Note that robust statistics, useful when data are affected by outliers, belongs to non-parametric statistics, at least in terms of statistical inference, because the behaviour of population distributions could be modelled by approximate models only. Non-parramelric Robust Parametric statistics statistics statistics The role of non-parametric, robust and parametric statistics. B) DISTRIBUTION-FREE SIGNIFICANCE TESTS There exist many different significance distribution-free tests, as above mentioned. Nevertheless for the sake of brevity, only few multiple significance distribution-free tests are presented in the following, concerning: ♦ ###goodness of fit of population distributions; ♦ ###independence against interdependence or dependence (e.g., collinearity); ♦ ###homogeneity of variance components for several variances; ♦ ###variance analysis of several means; where the last two tests are repeated for independent and correlated samples, respectively. The following figures show the partition of a population distribution (the normal function, in the example) into three regions: the null hypothesis central zone and two critical sided zones, and the sketch of a power curve (according to the normal function, in the example). Note that these schemes are basically always the same, although tests and population distributions are different in many ways. In the following m samples whose sizes are ni or if all samples have the same size, are considered. Power curve according to the normal function.

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