This book provides an introduction to the statistical analysis of quantitative data
for researchers studying aspects of language and language processing. The statistical
analysis of quantitative data is often seen as an onerous task that we would
rather leave to others. Statistical packages tend to be used as a kind of oracle, from
which you elicit a verdict as to whether you have one or more significant effects
in your data. In order to elicit a response from the oracle, you have to click your
way through cascades of menus. After a magic button press, voluminous output
tends to be produced that hides the p-values, the ultimate goal of the statistical
pilgrimage, among lots of other numbers that are completely meaningless to the
user, as befits a true oracle.
The approach to data analysis to which this book provides a guide is fundamentally
different in severalways. First of all, we will make use of a radically different
tool for doing statistics, the interactive programming environment known as R. R is
an open source implementation of the (object-oriented) S language for statistical
analysis originally developed at Bell Laboratories. It is the platform par excellence
for research and development in computational statistics. It can be downloaded
from the Comprehensive R Archive Network (cran) at http://cran.rproject.
org or one of the many mirror sites. Learning towork with R is in many
ways similar to learning a new language. Once you have mastered its grammar,
and once you have acquired some basic vocabulary, you will also have begun to
acquire a new way of thinking about data analysis that is essential for understanding
the structure in your data. The design of R is especially elegant in that it has a
consistent uniform syntax for specifying statistical models, no matter which type
of model is being fitted.