
This text began as notes for a course in statistical computing for second
year actuarial and statistical students at the University of Western
Ontario. Both authors are interested in statistical computing, both as support
for our other research and for its own sake. However, we have
found that our students were not learning the right sort of programming
basics before they took our classes. At every level from undergraduate
through Ph.D., we found that students were not able to produce simple,
reliable programs; that they didn’t understand enough about numerical
computation to understand how rounding error could influence their
results; and that they didn’t know how to begin a difficult computational
project.
We looked into service courses from other departments, but we found
that they emphasized languages and concepts that our students would not
use again. Our students need to be comfortable with simple programming
so that they can put together a simulation of a stochastic model; they also
need to know enough about numerical analysis so that they can do numerical
computations reliably.We were unable to find this mix in an existing course,
so we designed our own.
We chose to base this text on R. R is an opensource computing package
which has seen a huge growth in popularity in the last few years. Being open
source, it is easily obtainable by students and economical to install in our
computing lab. One of us (Murdoch) is a member of theRcore development
team, and the other (Braun) is a coauthor of a book on data analysis using
R. These facts made it easy for us to choose R, but we are both strong
believers in the idea that there are certain universals of programming, and
in this text we try to emphasize those: it is not a manual about programming
in R, it is a course in statistical programming that uses R. 


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