Critical Values For The F Distribution

Scheffé’s test ( Scheffé, 1953 ), which is commonly used to conduct post hoc contrasts among k group means, is unnecessarily conservative because it guards against an infinite number of potential post hoc contrasts when only a small set would ever be of interest to a researcher. This paper identifies a set of post hoc contrasts based on subsets of the treatment groups and simulates critical values from the appropriate multivariate F-distribution to be used in place of those associated with Scheffé’s test. The proposed method and its critical values provide a uniformly more powerful post hoc procedure.

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Exact Critical Values of the Hardy-Weinberg Test Statistic for two Alleles

Communications in Statistics - Simulation and Computation ◽

10.1080/03610917308548250 ◽

1973 ◽

Vol 1 (3) ◽

pp. 229-242

Author(s):

C. Vithayasai

Keyword(s):

Critical Values ◽

Test Statistic

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Critical Values

Quarterly Journal of Political Science ◽

10.1561/100.00000033 ◽

2008 ◽

Vol 3 (3) ◽

pp. 201-202

Author(s):

Keith Krehbiel

Keyword(s):

Critical Values

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Sebaran Peluang Acak Kontinu, Distribusi Normal, Distribusi Normal Baku, Distribusi T, Distribusi Chi Square, dan Distribusi F

10.31219/osf.io/grdnm ◽

2020 ◽

Author(s):

Ahmad Sudi Pratikno

Keyword(s):

Hypothesis Test ◽

Standard Normal Distribution ◽

Process Data ◽

Nominal Data ◽

Chi Square ◽

Stable Curve ◽

Standard Normal ◽

Research Findings ◽

F Distribution ◽

T Distribution

In statistics, there are various terms that may feel unfamiliar to researcher who is not accustomed to discussing it. However, despite all of many functions and benefits that we can get as researchers to process data, it will later be interpreted into a conclusion. And then researcher can digest and understand the research findings. The distribution of continuous random opportunities illustrates obtaining opportunities with some detection of time, weather, and other data obtained from the field. The standard normal distribution represents a stable curve with zero mean and standard deviation 1, while the t distribution is used as a statistical test in the hypothesis test. Chi square deals with the comparative test on two variables with a nominal data scale, while the f distribution is often used in the ANOVA test and regression analysis.

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Detecting Heterogeneity of Substitution Along DNA and Protein Sequences

Genetics ◽

10.1093/genetics/143.1.589 ◽

1996 ◽

Vol 143 (1) ◽

pp. 589-602 ◽

Cited By ~ 2

Author(s):

Peter J E Goss ◽

R C Lewontin

Keyword(s):

Null Hypothesis ◽

Nonuniform Distribution ◽

Critical Values ◽

Third Order ◽

Variance Test ◽

Data Set ◽

Significance Level ◽

Uniformly Most Powerful Test ◽

Interval Lengths ◽

Uniformly Most Powerful

Abstract Regions of differing constraint, mutation rate or recombination along a sequence of DNA or amino acids lead to a nonuniform distribution of polymorphism within species or fixed differences between species. The power of five tests to reject the null hypothesis of a uniform distribution is studied for four classes of alternate hypothesis. The tests explored are the variance of interval lengths; a modified variance test, which includes covariance between neighboring intervals; the length of the longest interval; the length of the shortest third-order interval; and a composite test. Although there is no uniformly most powerful test over the range of alternate hypotheses tested, the variance and modified variance tests usually have the highest power. Therefore, we recommend that one of these two tests be used to test departure from uniformity in all circumstances. Tables of critical values for the variance and modified variance tests are given. The critical values depend both on the number of events and the number of positions in the sequence. A computer program is available on request that calculates both the critical values for a specified number of events and number of positions as well as the significance level of a given data set.

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