The Prentice-Hall laboratory Lotus series contains, at present, two volumes, both by Mezei. His first text was Laboratory Lotus: a complete guide to instrument interfacing [1], and this work is his second. The author’s enthusiasm for Lotus is evident in his introduction.

Why use Lotus? Because of the freedom to evaluate and analyze data in the way you want to, without being constrained by the limitations of some specific software package. Use of Lotus enables new applications to be developed without having continually to learn new software commands, and it enables the user to tailor the software to what is happening in the experimental set-up.

For each technique discussed, the author starts with a theoretical overview. The second section tells “when to use the technique” and is followed by step-by-step instructions on creating the technique. Explanations of the detailed functioning of the spreadsheet or program are followed by an explanation of how to interpret the results.

The text progresses from general concepts (table look-up, templates, and distributions) through a number of statistical procedures and their assumptions, and linear and nonlinear curve fitting (including the creation of Lotus programs), and ends with advanced programming (such as nonparametric statistical tests and analysis of variance).

All examples in the book have been tested by the author using varieties of Lotus 1-2-3 and Symphony. The 13 appendices deal with Lotus itself, macros, and various statistical tables (t-distribution, f-distribution, chi-square, Poisson, Kolmogorov-Smirnov, et cetera). I am a little surprised that, for example, Lotus is used to evaluate a value of t but then recourse is made to the printed table to deduce the significance of that t value, but for the Kolmogorov-Smirnov test the author suggests typing the appropriate values into the spreadsheet.

The reader needs access to Lotus 1-2-3, Symphony, or both, and the willingness to work through the examples. The book is not intended to be a comprehensive text on derivations as the deep theoretical basis of statistics, linear regression, or calculus, but a practical guide on “how to implement these mathematical tools to real-life data.”