The standard approach of most introductory books for practical
statistics is that readers first learn the minimum mathematical
basics of statistics and rudimentary concepts of statistical
methodology. They then are given examples of analyses of data
obtained from natural and social phenomena so that they can grasp
practical definitions of statistical methods. Finally they go on to
acquaint themselves with statistical software for the PC and
analyze similar data to expand and deepen their understanding of
statistical methods.
This book, however, takes a slightly different approach, using
simulation data instead of actual data to illustrate the functions
of statistical methods. Also, R programs listed in the book help
readers realize clearly how these methods work to bring intrinsic
values of data to the surface. R is free software enabling users to
handle vectors, matrices, data frames, and so on.
For example, when a statistical theory indicates that an event
happens with a 5 % probability, readers can confirm the fact using
R programs that this event actually occurs with roughly that
probability, by handling data generated by pseudo-random numbers.
Simulation gives readers populations with known backgrounds and the
nature of the population can be adjusted easily. This feature of
the simulation data helps provide a clear picture of statistical
methods painlessly.
Most readers of introductory books of statistics for practical
purposes do not like complex mathematical formulae, but they do not
mind using a PC to produce various numbers and graphs by handling a
huge variety of numbers. If they know the characteristics of these
numbers beforehand, they treat them with ease. Struggling with
actual data should come later. Conventional books on this topic
frighten readers by presenting unidentified data to them
indiscriminately. This book provides a new path to statistical
concepts and practical skills in a readily accessible manner. "
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