CDN$ 172.76
  • List Price: CDN$ 215.95
  • You Save: CDN$ 43.19 (20%)
Usually ships within 4 to 7 weeks.
Ships from and sold by Amazon.ca.
Gift-wrap available.
Quantity:1
Have one to sell?
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See this image

Data Analysis with Excel®: An Introduction for Physical Scientists Hardcover – Mar 18 2002


See all 2 formats and editions Hide other formats and editions
Amazon Price New from Used from
Hardcover
"Please retry"
CDN$ 172.76
CDN$ 172.76

There is a newer edition of this item:


2014 Books Gift Guide
Yes Please, the eagerly anticipated first book from Amy Poehler, the Golden Globe winning star of Parks and Recreation, is featured in our 2014 Books Gift Guide. More gift ideas

Special Offers and Product Promotions

  • Join Amazon Student in Canada



Product Details

  • Hardcover: 464 pages
  • Publisher: Cambridge University Press (March 18 2002)
  • Language: English
  • ISBN-10: 0521793378
  • ISBN-13: 978-0521793377
  • Product Dimensions: 17.4 x 2.8 x 24.7 cm
  • Shipping Weight: 1.1 Kg
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • See Complete Table of Contents

Product Description

Review

"Overall I found the book excellent..." The Physicist

"Kirkup provides a very readable way for readers to learn basic principles of data analysis for the physical sciences and incoporates spreadsheets as flexible and powerful utilities...This excellent resource blends the power and utility of a popular spreadsheet package with relevant data analysis techniques and successfully combines content, relevance, and access to contemporary data analysis tools." Choice

Book Description

Data Analysis with Excel introduces techniques that are centrally important in the education of physical science students. Methods of data analysis are illustrated using the powerful spreadsheet package, Excel. Basic principles are reinforced using fully worked problems, and the underlying assumptions and range of applicability of techniques are discussed. Online support for the book covers further relevant topics. The book is suitable for undergraduate students in the physical sciences, but will also appeal to graduate students and researchers looking for an introduction to statistical techniques and the use of spreadsheets.

Inside This Book (Learn More)
Browse and search another edition of this book.
First Sentence
The principle of science, the definition almost, is the following: The test of all knowledge is experiment. Read the first page
Explore More
Concordance
Browse Sample Pages
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Search inside this book:

Customer Reviews

5.0 out of 5 stars
5 star
1
4 star
0
3 star
0
2 star
0
1 star
0
See the customer review
Share your thoughts with other customers

Most helpful customer reviews

By A Customer on Oct. 19 2003
Format: Paperback
If you are doing an engineering statistics course this book is of a hell of a lot more value than Engineering Statistics by Hubele, Montgomery and Runger.
This book teaches you how to do statistics using excel.
Should be aplicable for most statistics but is of great
assistance if your doing Engineering statistics and get stuck without much support.
Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again.

Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 2 reviews
21 of 21 people found the following review helpful
A disappointing book Nov. 12 2004
By A second reader - Published on Amazon.com
Format: Paperback
This is an introductory book on Excel for physical scientists. Cambridge University Press deserves a compliment for a beautifully produced volume. Unfortunately, its contents are disappointing, because the text contains serious errors and omissions. The most obvious error is the statement, on page 308, that Excel does not provide built in facilities for fitting equations to data using nonlinear least squares. Excel does provide these, in the form of Solver, but the reader will look in vain for any mention of Solver in this book. (Figure 9.1 on page 365 shows that the author indeed has not bothered to activate the Solver Add-in.) The most serious omission is that the existence of user-definable functions and macros is not mentioned either. This leaves out two of the most powerful features of Excel: nonlinear least squares, and user programmability.

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.
1 of 4 people found the following review helpful
A must for engineering statistics Oct. 19 2003
By A Customer - Published on Amazon.com
Format: Paperback
If you are doing an engineering statistics course this book is of a hell of a lot more value than Engineering Statistics by Hubele, Montgomery and Runger.
This book teaches you how to do statistics using excel.
Should be aplicable for most statistics but is of great
assistance if your doing Engineering statistics and get stuck without much support.


Feedback