Preface
There are many books on regression and analysis of variance. These books expect different levels of preparedness
and place different emphases on the material. This book is not introductory. It presumes some
knowledge of basic statistical theory and practice. Students are expected to know the essentials of statistical
inference like estimation, hypothesis testing and confidence intervals. A basic knowledge of data analysis is
presumed. Some linear algebra and calculus is also required.
The emphasis of this text is on the practice of regression and analysis of variance. The objective is to
learn what methods are available and more importantly, when they should be applied. Many examples are
presented to clarify the use of the techniques and to demonstrate what conclusions can be made. There
is relatively less emphasis on mathematical theory, partly because some prior knowledge is assumed and
partly because the issues are better tackled elsewhere. Theory is important because it guides the approach
we take. I take a wider view of statistical theory. It is not just the formal theorems. Qualitative statistical
concepts are just as important in Statistics because these enable us to actually do it rather than just talk about
it. These qualitative principles are harder to learn because they are difficult to state precisely but they guide
the successful experienced Statistician.
Data analysis cannot be learnt without actually doing it. This means using a statistical computing package.
There is a wide choice of such packages. They are designed for different audiences and have different
strengths and weaknesses. I have c