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Business Analytics: Data Analysis & Decision Making,
Become a master of data analysis, modeling, and spreadsheet use with BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 5E! This quantitative methods text provides users with the tools to succeed with a teach-by-example approach, student-friendly writing style, and complete Excel 2013 integration. It is also compatible with Excel 2010 and 2007. Problem sets and cases provide realistic examples to show the relevance of the material. The Companion Website includes: the Palisade DecisionTools Suite (@RISK, StatTools, PrecisionTree, TopRank, RISKOptimizer, NeuralTools, and Evolver); SolverTable, which allows you to do sensitivity analysis; data and solutions files, PowerPoint slides, and tutorial videos.
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First published March 15, 2012
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December 21, 2021Considering that I've barely read over the last few months because all my free time is taken up with my course I'm putting in the text book I've had to read for my end of year stats! This should really count as two books imo! 🙃
January 25, 2026
I read through this textbook to find out what "analytics" was. I was more used to the term "operations research" (in which I got a PhD in 1993), but around 2010 I started hearing the new term more and more, and it seemed to be applied to techniques that I had studied under the old label. So I looked through available analytics texts and picked this one because I was familiar with the popular OR texts written by co-author Winston, having used, taught from, and liked them.
So what I found was that analytics consists of two things: applications of OR techniques such as I had studied, plus a great deal of material on how to use Excel (sometimes with readily available add-ins) to handle and analyze data. Now spreadsheets were just coming into general use when I was a student, and I have found them to be extremely powerful and flexible tools, so I think this emphasis is perfect for an undergraduate text. The book gives a great introduction to the analytical tools in Excel. There is no attention to the mathematical underpinnings of the solution methods (which were the bulk of my studies), but I guess that's appropriate for the intended audience. There is much material on formulation, and due attention to sensitivity analysis. There are many examples worked out in detail and many problems for the student to work out. I only found a few errors in the examples, and those were minor.
Some things particularly delighted me. They used a paper I had co-authored for one of their extended examples--how could I not like that? And there was an interesting check on the effect of different random variable distributions when they have the same mean and standard deviation. This is something I've done a little work on too.
Then there were a few choices I found odd. They introduce evolutionary optimization in the last chapter (Data Mining) with no discussion of its limitations or hazards, or how it relates to other optimization methods. Also, they use simulation to estimate the chance of winning at craps. That's a problem I would naturally solve analytically. In fact, it would be a great problem to solve both ways, to compare-and-contrast analytical vs. simulation solutions. There's altogether too little emphasis on the fact that the result of a simulation is one sample from an unknown distribution, and statistical techniques are what is required for analyzing simulation results. Finally, there is no material on queues, which are quite common in the analysis of real business operations and offer many powerful analytical techniques.
So what I found was that analytics consists of two things: applications of OR techniques such as I had studied, plus a great deal of material on how to use Excel (sometimes with readily available add-ins) to handle and analyze data. Now spreadsheets were just coming into general use when I was a student, and I have found them to be extremely powerful and flexible tools, so I think this emphasis is perfect for an undergraduate text. The book gives a great introduction to the analytical tools in Excel. There is no attention to the mathematical underpinnings of the solution methods (which were the bulk of my studies), but I guess that's appropriate for the intended audience. There is much material on formulation, and due attention to sensitivity analysis. There are many examples worked out in detail and many problems for the student to work out. I only found a few errors in the examples, and those were minor.
Some things particularly delighted me. They used a paper I had co-authored for one of their extended examples--how could I not like that? And there was an interesting check on the effect of different random variable distributions when they have the same mean and standard deviation. This is something I've done a little work on too.
Then there were a few choices I found odd. They introduce evolutionary optimization in the last chapter (Data Mining) with no discussion of its limitations or hazards, or how it relates to other optimization methods. Also, they use simulation to estimate the chance of winning at craps. That's a problem I would naturally solve analytically. In fact, it would be a great problem to solve both ways, to compare-and-contrast analytical vs. simulation solutions. There's altogether too little emphasis on the fact that the result of a simulation is one sample from an unknown distribution, and statistical techniques are what is required for analyzing simulation results. Finally, there is no material on queues, which are quite common in the analysis of real business operations and offer many powerful analytical techniques.
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