Hmm. That reads like an exam question. None the less, since it was interesting to code, here's how to do section d):

#! perl -sw
use strict;
use List::Util qw[ min ];
use Data::Dump qw[ pp ];

my %pvalues = (
    1=> 0.5453980,
    2=> 0.4902384,
    3=> 0.8167950,
    4=> 0.2821822,
    5=> 0.4693030,
    6=> 0.6491767,
    7=> 0.9802138,
    8=> 0.1155778,
    9=> 0.9585124,
    10=> 0.4069490
);

my @orderedKeys = sort {
    $pvalues{ $b } <=> $pvalues{ $a }
} keys %pvalues;

my $d = my $n = values %pvalues;

$pvalues{ $_ } *= $n / $d-- for @orderedKeys;

$pvalues{ $orderedKeys[ $_ ] } =
    min( @pvalues{ @orderedKeys[ 0 .. $_ ] } )
    for 1 .. $n-1;


pp \%pvalues;

__END__
c:\test>junk68
{
  1  => "0.908996666666667",
  2  => "0.908996666666667",
  3  => "0.9802138",
  4  => "0.908996666666667",
  5  => "0.908996666666667",
  6  => "0.927395285714286",
  7  => "0.9802138",
  8  => "0.908996666666667",
  9  => "0.9802138",
  10 => "0.908996666666667",
}
[download]

I haven't done the last step, (I can't see the stuff in red), so you'll have to work out how to do that yourself. And to do that, you'll first need to understand how the new line above works. And if you can do that, you'll stand some chance of explaining it to whomever is going to check your work.

That'll be $25 :)

BTW:If this is going to be used for real on large volumes of data (which R code often is), then you'll want to replace the use of List::Utilmin() with a custom min() that doesn't use a list for input. Throwing large lists around is a sure-fire way to kill performance. That said, if the lists are large, then all the nested slicing is going to kill you anyway.

For real performance you might consider recoding this for PDL, but that's definitely left as an exercise.