- Taschenbuch: 468 Seiten
- Verlag: Cambridge University Press (7. März 2002)
- Sprache: Englisch
- ISBN-10: 0521797373
- ISBN-13: 978-0521797375
- Größe und/oder Gewicht: 17,4 x 2,8 x 24,7 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
- Amazon Bestseller-Rang: Nr. 2.890.376 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
- Komplettes Inhaltsverzeichnis ansehen
Data Analysis with Excel?: An Introduction for Physical Scientists (Englisch) Taschenbuch – 7. März 2002
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'This book is extremely well structured. It both describes the main functionality of Excel with special emphasis on scientific data analysis, as well as the statistical background to the methods … definitely one of the best on the market in this important area … the author should be congratulated on doing a wonderful job.' Chemistry & Industry
Overall, I found the book excellent.' S. Middleton, The Physicist
'Any scientist familiar with Microsoft windows who has Excel® installed will find in this book a simple and straightforward introduction to the use of spreadsheet calculations and data display … The coverage of this book will be more than adequate for undergraduate courses and will be sufficient for many postgraduate and research readers … This book gives a good introduction to Excel® for data analysis for the first time user and covers the data analysis methods that most physical scientists will need. The book is a useful reference for those who already use Excel® and for those who may want to extend the data analysis methods that they use.' Contemporary Physics
Über das Produkt
An essential introduction to data analysis techniques using a powerful spreadsheet package. Basic principles are reinforced using fully worked problems, exercises and online support. Suitable for undergraduate students, it will also appeal to graduate students and researchers seeking an introduction to statistical techniques and the use of spreadsheets.Alle Produktbeschreibungen
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Another major problem with this book is that it doesn't show the reader how to use the spreadsheet effectively, but often goes out of its way to make easy things difficult. The almost exclusive emphasis in this book is on least squares methods, yet these are handled quite clumsily. On page 244, e.g., the linear correlation coefficient is computed from its formula by calculating the necessary sums, rather than by taking advantage of the fact that Linest, Regression, and Trendline all provide this parameter or its square. On page 284 the reader is shown the matrix algebra for fitting data to a parabola, and then told that "The built in matrix functions of Excel are well suited to estimating parameters in linear least squares problems", as if Linest, Regression, and Trendline are not there to take care of such tedious data manipulations. Likewise, on page 290, the user is not informed that Linest and Regression can also do multivariate analysis, but instead is instructed to do this the hard way, again by setting up and solving matrix equations. It is as if the author hasn't quite figured out yet that the spreadsheet has several built-in facilities specifically designed to make such least squares problems user-friendly.
In comparison with other books vying for the scientific spreadsheet market it is difficult to come up with any area in which Kirkup's book has the edge over its competitors: Billo (2nd ed., Wiley, 2001), Bloch (2nd ed., Wiley, 2003), de Levie (Oxford, 2004), Gottfried (2nd ed., McGraw-Hill, 2002), Liengme (3rd ed., Newnes, 2002), and Orvis (2nd ed., Sybex, 1996) all provide much more useful information, and don't make their readers jump through unnecessary hoops either.