Tables of Percentage Points of the Incomplete Beta-Function

Thompson, Catherine M.; Pearson, E. S.; Comrie, L. J.; Hartley, H. O.

doi:10.2307/2332208

Cited by 87 publications

(22 citation statements)

References 1 publication

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“…At the beginning of each interview, a respondent was asked for his current logistics cost level to place the experiment for the respondent in a realistic context. The estimation procedure 3 We use the standard table of the V 2 distribution published by Thompson (1941) [42]. 4 The degrees of freedom refer to the number of coefficients estimated in a model.…”

Section: Resultsmentioning

confidence: 99%

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Logistics collaboration decisions: not a fully rational choice

Visser

2010

Logist. Res.

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Most existing models in supply chain management literature proving the potential of vertical logistics collaboration rely on the assumptions of neoclassical or new institutional economic theory. These theories see individual decision makers as fully rational agents. Nevertheless, reviewing the decision-making-behavior literature makes clear that individual decision makers also derive utility from behavioral factors and display inertia in their choices. This paper takes up this issue and quantitatively measures the impact of behavioral factors and inertia on vertical logistics collaboration decision between shipper and logistics service provider. A stated preference experiment is used to reach our research objective. The results of the experiment show that the behavioral factors like trust, confidentiality and commitment significantly impact a logistics collaboration decision. Next to this, the stated preference confirms that shippers leave beneficial collaboration opportunities unexploited because of a certain level of inertia. It may be concluded that both aspects function as a threshold to intensify collaboration between shipper and logistics service provider.

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Section: Resultsmentioning

confidence: 99%

“…To determine whether the delta of the Loglikelihood value is a significant improvement or not, a standard table of the X 2 distribution function is used [42] …”

Section: Appendixmentioning

confidence: 99%

Logistics collaboration decisions: not a fully rational choice

Visser

2010

Logist. Res.

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“…For example, there are many contributions that provide tables of quantiles for various levels of significance and degrees of freedom at various levels of accuracy. Some of the earlier contributions include those from Pearson (1922), Fisher (1928, Thompson (1941), Merrington (1941), Aroian (1943), Goldberg and Levine (1946), Hald and Sinkbaek (1950), Vanderbeck and Cooke (1961), Harter (1964) and Krauth and Steinebach (1976). While these contributions give tables of quantiles, others provide simple formulae for calculating these values for any level of significance; see, for example, Wilson and Hilferty (1931) and Heyworth (1976).…”

Section: Overview Of Pearson's Chi-squared Statistic and Its P-valuementioning

confidence: 99%

Exploring How to Simply Approximate the P-value of a Chi-squared Statistic

Beh

2018

AJS

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Calculating the p-value of any test statistic is of paramount importance to all statistically minded researchers across all areas of study. Many, these days, take for granted how the p-value is calculated and yet it is a pivotal quantity in all forms of statistical analysis. For the study of 2×2 tables where dichotomous variables are assessed for association, the chi-squared statistic, and its p-value, are fundamental quantities to all analysts, especially those in the health and allied disciplines. Examining the association between dichotomous variables is easily achieved through a very simple formula for the chi-squared statistic and yet the p-value of this statistic requires far more computational effort. This paper proposes and explores a very simple approximation of the p-value for a chi-squared statistic given its degrees of freedom. After providing a review of a variety of common ways for determining the quantile of the chi-squared distribution given the level of significance and degrees of freedom, we shall derive an approximation based on the classic quantile formula given in 1977 by D. C. Hoaglin. We examine this approximation using a simple 2×2 contingency table example then show that it is extremely precise for all chi-squared values ranging from 0 to 50.

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“…This probability a of making an incorrect statement may be made small by choosing a large value for the constant b, or by obtaining a large sample of n serial numbers. We might first determine how small the probability a of making an incorrect statement should be, and then determine b or n from the relation a =nb l-n +(l-n)b-n • Tables are available which will simplify the computations (see [5], [6]). A reprint of [6] may be purchased from Biometrika.…”

Section: Confidence Intervalsmentioning

confidence: 99%

Some Practical Techniques in Serial Number Analysis

Goodman

1954

Journal of the American Statistical Association

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Tables of Percentage Points of the Incomplete Beta-Function

Cited by 87 publications

References 1 publication

Logistics collaboration decisions: not a fully rational choice

Logistics collaboration decisions: not a fully rational choice

Exploring How to Simply Approximate the P-value of a Chi-squared Statistic

Some Practical Techniques in Serial Number Analysis

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