Some Statisticians have prepared a set of tables called Tables of Random Numbers. A sample os framed with reference to these tables. Of all these table Tippet's Table is most widely used. Using 41,600 figures, Tippet has involved 10,400 numbers comprising of four units each. For the use if this method, all items of the universe are first arranged in an order. Then using Tippet Table the required number of items are selected as are needed for a sample. -Ishika Shree
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Most computers generate pseudo-random numbers - these are numbers which are created using a formula, but due to the way the formula works, the sequence of numbers generated appears random and is good enough for most applications. The random number generator can be seeded so that the same sequence of "random" numbers is generated every time. Some systems improve on this by using unpredictable "real-world" events to create a more truly random sequence: The Apple ][ computer when waiting for a key press from the user would keep incrementing the current "seed"; thus the seed was influenced by the random event of the user pressing a key but if a series of "random" numbers was then taken, they were strictly pseudo-random. Linux has a pseudo-random number generator in a library function, but it also has in the kernel itself an "entropy pool" which is filled by environmental "noise" created by device drivers, etc. By accessing /dev/random a series of numbers is created from this pool; if the pool empties then the device will block until more "Noise" has been collected. /dev/urandom acts similarly, except that if the pool empties, then it falls back onto a pseudo-random sequence. As the entropy pool is limited in size, the random values being read should be used where security is important, eg in creating the key for an encryption, in small doses.
You can't. The ball numbers are generated in a way that is almost as random as is humanly possible.
2x19
6
A conversion table.A conversion table.A conversion table.A conversion table.