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Wilcoxon Rank-Sum Test

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Title: STT 430/530, Nonparametric Statistics Author: Dargan Frierson, Jr. Last modified by: Dargan Frierson Created Date: 1/7/2004 12:08:44 PM Document presentation ... – PowerPoint PPT presentation

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Title: Wilcoxon Rank-Sum Test


1
Wilcoxon Rank-Sum Test
  • If X1, X2, Xn is a sample of size n from a
    population, then the rank of Xi , R(Xi), is given
    by
  • R(Xi) number of Xjs Xi for each i.
    Compute midranks if there are tied values (i.e.,
    average the ranks - later). The Wilcoxon
    statistic is based on the sum of the ranks of the
    data in one of the two groups
  • Here's the process
  • pool the m observations from group1 with the n
    observations from group2 (total of mn) and order
    them all from smallest to largest.
  • assign ranks (or midranks) to the ordered data
    smallestrank1, next smallestrank 2, etc.
  • Let W sum of the ranks of the observations from
    group1 (or group2)
  • determine the p-value associated with your value
    of W and decide whether to reject the null
    hypothesis of no difference in the two population
    distributions
  • if there are no ties, the p-value can be computed
    by looking at the actual distribution of all the
    permutations of the mn ranks or
  • by looking at many samples of permutations of
    ranks or
  • by looking in Table A3 of the Appendix of our
    book
  • Let's go over Example 2.4.2 on page 38hypothesis
    is that there is no difference in distributions
    of dry weights of herbicide treated and untreated
    plants.

2
  • The alternative is that the untreated plants will
    have a larger distribution of weights, so we'll
    use an upper-tailed test. Table A3 has critical
    values (both upper and lower) for a.05, .025 and
    .01 when m,n range from 4 to 10 more extensive
    tables are available in the library and R and SAS
    will both give exact (and approximate) p-values
    for the Rank-Sum test.
  • Look at the R code for doing the Wilcoxon test in
    the R3 document
  • Let's look at the SAS solution to this
  • dm output 'clear' dm log 'clear'
  • options ls80
  • data ex2_4_2
  • input group weight _at__at_
  • datalines
  • u .55 u .67 u .63 u .79 u .81 u .85 u .68
  • t .65 t .59 t .44 t .60 t .47 t .58 t .66 t .52 t
    .51
  • proc rank dataex2_4_2 outrankwts var weight
    ranks rwt run
  • proc sort datarankwts by group run
  • proc print datarankwts by group sumby group
    run
  • proc npar1way wilcoxon exact
  • class group var weight run quit
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