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Quantitative Methods is a comprehensive guide to the techniques any student of business or finance is likely to need. The authors' coaching, learning-by-doing approach coupled with the text's clear structural outline makes these essential mathematical skills far less daunting.
A bestselling and popular text in its previous editions, it has been fully updated with:
* new 4 color text design
* New and improved Companion Website
* More empirical examples, from across the business, management and finance spectrum
* VLE-compatible web content for Blackboard and WebCT
* Updated commands and exercises in light of new version Excel and SPSS
* New content on: data mining, OLAP, text mining, non-parametric methods, index numbers, productivity software tools.
With its learn-by-doing approach, this guide will appeal to a wide audience, from undergraduates of business and finance to postgrad students studying quants.
- Sales Rank: #1023958 in Books
- Published on: 2010-03-15
- Released on: 2010-03-02
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 1.00" w x 7.00" l, 3.56 pounds
- Binding: Paperback
- 912 pages
Review
“Far too many students steer clear of quantitative methods because they fear that they will be unable to cope with the complexities of statistics and formulae. Louise Swift and Sally Piff have managed to produce a wonderfully clear text that takes students gently through the basic principles behind the most commonly used quantitative methods, simultaneously providing the basis for an understanding of statistical outputs which every researcher requires, regardless of the methods they chose to use in their own work.” —Tony Bryant, Professor of Informatics, Leeds Metropolitan University, UK
About the Author
LOUISE SWIFT taught Quantitative Methods to students of business, management and finance for over ten years at the University of East Anglia, UK, where she now works as a statistician.
SALLY PIFF is the Quantitative Methods tutor in the School of Management, University of East Anglia, UK.
Most helpful customer reviews
8 of 8 people found the following review helpful.
Outstanding Value for What's Included vs. other key texts
By Let's Compare Options Preptorial
I've taught both MBA and Master's in Management classes online and physically at MIT, Harvard (GE Crotonville) and Phoenix State University. There are a group of about 3-5 texts that are now the most popular for graduate Quantitative Analysis (QA). These include the Swift text (most popular in Europe) at from $40 to $80 US (Quantitative Methods: For Business, Management and Finance), the Render Quantitative Analysis for Management text (Quantitative Analysis for Management (11th Edition)), $175 for the 11th edition, and the very thorough but NOT for self study Anderson text (Quantitative Methods for Business (with Printed Access Card)) for $250.
QA is a really tough course for people with less math background such as liberal arts, nursing, etc. students going for an MBA, and a bit easier for math and engineering majors in undergrad, because it involves modeling, using a lot of fairly advanced math. Typical QA courses include linear programming (not really linear, and not really programming-- it is about objectives and constraints stated algebraically to find "optimal" graphic or corner solutions to problems such as mixing, profit maximization, or cost minimization); queueing problems; statement analysis; Decision analysis; Project Management; Forecasting; Inventory Control; Logistics; Markov Analysis; Stats; Integer/ nonlinear/ goal programming; regression modeling; simulation; probability, and possibly some econometrics. Most grad (MBA) courses don't go as far as differential equation modeling (frequent models in physics, biosciences, ecology, etc).
The Swift and Render texts are the most popular, and the Anderson text a distant third according to publisher's (QA!) data on course use and sales. Both Swift and Render are frequently zinged in reviews for broken or less useful software and models, but this is an unfair criticism. Swift gives numerous (although London oriented) model examples, and Render has their own internet models available. The reason both of these criticisms are unfair is that there are numerous plug ins for Excel that do all the analysis, statistics, programming solutions, etc. you will need regardless of which text you use. The Library Picks team chose Render as the top pick, but due to expense, if you're doing self study, Swift is also a good bet.
If the included websites, plug ins, templates and downloads aren't working for you from a software/ modeling viewpoint, here is a brief list of outstanding Excel options that will do the same thing, many for free:
-- Statistical Analysis: Microsoft Excel 2010
-- Business Analysis: Microsoft Excel 2010 (MrExcel Library)
-- Predictive Analytics: Microsoft Excel
-- Microsoft Excel 2010: Data Analysis and Business Modeling
-- Charts and Graphs: Microsoft Excel 2010 (MrExcel Library)
These will take care of the problems metioned by some reviewers that it is hard to stay up to date on models. In particular, Microsoft has now migrated their SQL server business analysis package to a new language called DAX in the FREE Excel plug in PowerPivot. These plug ins, many free on the web, will work perfectly with the examples in Swift and Render so you don't have to sweat the included QA software if it crashes.
One caveat: many reviews complain about QA software with "bugs" when in fact it is the reviewer's computer at fault. To run modern QA programs with lots of modeling, you need a modern dual core processor with at LEAST 8 gig of ram, preferably 12 or 16. The reason for this is that with pipelining, the old "math coprocessor" hardware has given way to sharing between CPU and GPU for floating point, and recent software-- even SPSS -- will crash older machines or appear to be buggy. Excel with a plug in also is a great answer to this, because you can get away with much less power if your machine is already doing well with Excel. PowerPivot is intensive, but only if you have huge amounts of data. The typical mixing problem is no problem even with a computer that's say, post 2009.
0 of 0 people found the following review helpful.
Great book
By Scott Steele
This is one of the best books I've ever read. The book is a completely practical guide that begins with an excellent primer/review on the math you will need to be successful in the subsequent chapters. This may be the best book on its subject as far as an applied approach is concerned.
0 of 0 people found the following review helpful.
Good book
By Jather Ian Banda
It explains the concepts & gives examples. Its 1 of the recommend books @ our university I will definitely improve & do better. This term.
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