Tables of Range and Studentized Range

Harter, H. Leon

doi:10.1214/aoms/1177705684

Cited by 152 publications

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“…For multiple comparisons, nonparametric multiple comparisons by STP were performed, as described by Sokal and Rohlf (1995). Additional tables of critical values of the studentized range were obtained from Harter (1960). Because nonparametric multiple comparisons by STP require equal sample sizes, samples were randomized and then truncated to the size of the smallest sample included in the analysis.…”

Section: Discussionmentioning

confidence: 99%

Generation of Signaling Specificity in Arabidopsis by Spatially Restricted Buffering of Ligand–Receptor Interactions

2011

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Core signaling pathways function in multiple programs during multicellular development. The mechanisms that compartmentalize pathway function or confer process specificity, however, remain largely unknown. In Arabidopsis thaliana, ERECTA (ER) family receptors have major roles in many growth and cell fate decisions. The ER family acts with receptor TOO MANY MOUTHS (TMM) and several ligands of the EPIDERMAL PATTERNING FACTOR LIKE (EPFL) family, which play distinct yet overlapping roles in patterning of epidermal stomata. Here, our examination of EPFL genes EPFL6/CHALLAH (CHAL), EPFL5/CHALLAH-LIKE1, and EPFL4/CHALLAH-LIKE2 (CLL2) reveals that this family may mediate additional ERdependent processes. chal cll2 mutants display growth phenotypes characteristic of er mutants, and genetic interactions are consistent with CHAL family molecules acting as ER family ligands. We propose that different classes of EPFL genes regulate different aspects of ER family function and introduce a TMM-based discriminatory mechanism that permits simultaneous, yet compartmentalized and distinct, function of the ER family receptors in growth and epidermal patterning.

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Section: Discussionmentioning

confidence: 99%

Generation of Signaling Specificity in Arabidopsis by Spatially Restricted Buffering of Ligand–Receptor Interactions

2011

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“…Conversely, the determination of the expected value and variance of extreme values generally presents numerical problems, whereas those of the range can be derived from the extreme values by simple calculation. For normal distributions, all these characteristic values are largely available (see e. g. 1. c. (3)(4)(5)). For standard normal distribution and for the above-mentioned random sample range, the expected value μ (η) for extreme values, and the quantiles T (n) (90) and T (n) (95) for 90% and 95%, respectively, can be taken from table 1, which also gives the probability P (n) (2.0) that a minimal deviation of two standard deviations will occur above (for X (n) ) and below (for X (1) ) the central value.…”

Section: )mentioning

confidence: 99%

“…The relevant values can be taken from the literature, in particular from 1. c. (5). For a normal distribution the quantile T R (90) and T R (95) for 90% and 95% respectively, i.e.…”

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confidence: 99%

Detection of Short-Term Fluctuations in Measurement Systems by Determination of Extreme Values and Range

Hansert¹

1996

CCLM

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Summary:In the new draft, in preparation, of the guidelines of the German Medical Council (Richtlinien der Bundesärztekammer, RiLiBÄK) for quality management of clinical laboratory measurement systems, particular emphasis is placed on the establishment of a lower and upper limit for each control value in a control cycle. In the present study, an attempt is made to show that this approach alone does not detect even relatively large short-term changes in a measurement system. A substantial improvement is possible with modest outlay, by also examining the difference between the largest and smallest individual control values, i. e. by determining the current range. Appropriate practical limits for this procedure are derived from the theoretical behaviour of extreme values, based initially on the usual assumption of a normal distribution. Furthermore, it is shown that these limits are also meaningful for other assumed appropriate distributions.

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“…The correct value of q that is required as input can be obtained from the extensive tables of Harter, Clernm, and Guthrie (1959) or from more accessible tables in Harter (1960), Pearson and Hartley (1966 , Table 29), and some statistics texts. An approximation routine could be included to estimate the q value so that the user would not have to provide that value.…”

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confidence: 99%

“…(A significant overall F test is not, however, required before using Tukey's procedure.) The means and sample sizes were entered into the program, together with the values of 2.98 for S2, taken from the ANOYA, and 3.791 for q, taken from Harter (1960Harter ( , p. 1134. The results displayed by the program are shown in Table 2.…”

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confidence: 99%

A BASIC program for Tukey’s multiple comparison procedure

Brophy¹

1984

Behavior Research Methods, Instruments, & Computers

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Tables of Range and Studentized Range

Cited by 152 publications

References 0 publications

Generation of Signaling Specificity in Arabidopsis by Spatially Restricted Buffering of Ligand–Receptor Interactions

Generation of Signaling Specificity in Arabidopsis by Spatially Restricted Buffering of Ligand–Receptor Interactions

Detection of Short-Term Fluctuations in Measurement Systems by Determination of Extreme Values and Range

A BASIC program for Tukey’s multiple comparison procedure

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