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Applications of Normality Test in Statistical Analysis 被引量:4
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作者 Nasrin Khatun 《Open Journal of Statistics》 2021年第1期113-122,共10页
In this study, to power comparison test, different univariate normality testing procedures are compared by using new algorithm. Different univariate and multivariate test are also analyzed here. And also review effici... In this study, to power comparison test, different univariate normality testing procedures are compared by using new algorithm. Different univariate and multivariate test are also analyzed here. And also review efficient algorithm for calculating the size corrected power of the test which can be used to compare the efficiency of the test. Also to test the randomness of generated random numbers. For this purpose, 1000 data sets with combinations of sample size n = 10, 20, 25, 30, 40, 50, 100, 200, 300 were generated from uniform distribution and tested by using different tests for randomness. The assessment of normality using statistical tests is sensitive to the sample size. Observed that with the increase of n, overall powers are increased but Shapiro Wilk (SW) test, Shapiro Francia (SF) test and Andeson Darling (AD) test are the most powerful test among other tests. Cramer-Von-Mises (CVM) test performs better than Pearson chi-square, Lilliefors test has better power than Jarque Bera (JB) Test. Jarque Bera (JB) Test is less powerful test among other tests. 展开更多
关键词 Normality Test univariate Test Multivariate Test
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Diversity, composition and host-species relationships of epiphytic orchids and ferns in two forests in Nepal 被引量:1
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作者 Yagya P. ADHIKARI Anton FISCHER +3 位作者 Hagen S.FISCHER Maan B.ROKAYA Prakash BHATTARAI Axel GRUPPE 《Journal of Mountain Science》 SCIE CSCD 2017年第6期1065-1075,共11页
Epiphytic plant species are an important part of biological diversity. It is therefore essential to understand the distribution pattern and the factors influencing such patterns. The present study is aimed at observin... Epiphytic plant species are an important part of biological diversity. It is therefore essential to understand the distribution pattern and the factors influencing such patterns. The present study is aimed at observing the patterns of species richness, abundances and species composition of epiphytic orchids and ferns in two subtropical forests in Nepal. We also studied the relationship of host plants(Schima wallichii and Quercus lanata) and epiphyte species. Data were collected in Naudhara community forest(CF) and the national forest(NF) in Shivapuri Nagarjun National Park. The data were analyzed using univariate and multivariate tests. In total, we recorded 41 species of epiphytes(33 orchid and 8 fern species). Orchid species abundance is significantlyhigher in CF compared to NF. Orchid species richness and abundance increased with increasing southern aspect whereas it decreased with increasing canopy cover, and fern species richness increased with host bark roughness. Orchid abundance was positively correlated with increasing bark p H, stem size, tree age and tree height and negatively correlated with increasing steepness of the area. Likewise, fern abundances were high in places with high canopy cover, trees that were tall and big, but decreased with increasing altitude and southern aspect. The composition of the orchid and fern species was affected by altitude, aspect, canopy cover, DBH, number of forks and forest management types. We showed that the diversity of orchid and fern epiphytes is influenced by host characteristics as well as host types. The most important pre-requisite for a high epiphyte biodiversity is the presence of oldrespectively tall trees, independent of the recent protection status. This means:(i) for protection, e.g.in the frame of the national park declaration, such areas should be used which host such old tall trees;and(ii) also in managed forests and even in intensively used landscapes epiphytes can be protected by letting a certain number of trees be and by giving them space to grow old and tall. 展开更多
关键词 Environmental factors Epiphytes Large trees Indicator species Multivariate and univariate analyses Permutations tests
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