Bayes and Empirical Bayes Methods for Data Analysis, Second Edition Review

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(More customer reviews)This book features a deep and focused lesson on Bayes and Empirical Bayes Methods. It goes through the key topics as conjugate priors, MCMC methods (non iteratives and iteratives as the well known Gibbs samplining and metropolitis hastings algorithms), model selection methods (as bayes factor) and issues related as model robusteness.
The Approach is increasingly formal and deeply complex, allowing for getting the basics or diving into more complex knowledge according to your former background. You need at least a good understanding of Frequentist statistic to be able to follow the reasonings. Each chapter allow you to stop at some point without losing the thread. Last part of the book is in fact deep knowledge demanding.
The most interesting point of this book according to my very limited statistics background is that it makes good comparations with the frequentist approach (classical approaches as confidence intervals and point estimators), checking performance of either method. Even, it features some combination of both approaches getting some bayessian intervals.
As a negative point, I would say that examples are hard to follow for someone with limited bakground and too much complex. They really do not clear me up enough.
All in all, is a very profitable book for jumping into bayesian methods.

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In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. Building on the first edition of their popular text, Carlin and Louis introduce these methods, demonstrate their usefulness in challenging applied settings, and show how they can be implemented using modern Markov chain Monte Carlo (MCMC) methods. Their presentation is accessible to those new to Bayes and empirical Bayes methods, while providing in-depth coverage valuable to seasoned practitioners.With its broad appeal as a text for those in biomedical science, education, social science, agriculture, and engineering, this second edition offers a relatively gentle and comprehensive introduction for students and practitioners already familiar with more traditional frequentist statistical methods. Focusing on practical tools for data analysis, the book shows how properly structured Bayes and EB procedures typically have good frequentist and Bayesian performance, both in theory and in practice.

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Beautiful Data: The Stories Behind Elegant Data Solutions Review

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(More customer reviews)"Beautiful Data" is a collection of essays on data; how people have transformed it, worked within its confines, and offers a glimpse of where we might go. Many of the essays are wonderful snippets into how some people perceive data while others fall flat. Overall its a mostly enjoyable read that helps open up your mind to new potentials.
First a disclaimer; I am not a data person. However I've been involved, fairly heavily, in the data field. In the parlance of the world, I'm a back end person. However I'm always trying to think about the front end; how will things be used and what information can we gleen from the system (or systems). With that in mind, this is a book that speaks to me - its all about the front end.
Some of the best essays in the book would be:
The first essay by Nathan Yau he talks very much about user created data and personal databases (knowledge bases). What's exciting here is how he takes data already out there, data you have provided, and creates something useful and yes, beautiful, out of it.
The Second essay by Follett and Holm really gets down to how if you want the data, you need to present it in a way that brings people into the process. As someone who has a slight crush on the statistics and practices in polling (and designing poll questions) this essay really was a fascinating read.
The third essay by Hughes detailed how he handled images on the Mars mission. There wasn't anything here that wasn't done in embedded systems 15 years ago; still it was a great walk down memory lane since I used to program embedded imaging systems.
Chapter 4 really hit home PNUTShell is cloud storage and data processing in real time. This really is the stuff of the future.
Chapter 5 by Jeff Hammerbacher really didn't offer too many insights but his writing style is fluid and fun plus he offered a glimpse into how Facebook grew.
We then have the slow section of the book - Chapter 8 on distributed social data had promise but it read more like a company white page than an interesting article. Same with Chapter 12 [...].
Thankfully chapter 10 on Radiohead's "House of Cards" video was there - and here we are presented with true beauty in data - beautiful enough to create a music video out of!
I'm still on the fence with Chapter 13 - What Data Doesn't Do. It was an interesting chapter but it felt both too long and too short at the same time. I almost felt that in the author, Coco Krumme, were to write a book on this topic, I'd want to read it. However her essay was not the right vehicle.
Finally, the last chapter - "Connecting Data" was a truly inspiring piece; one that offers up paths for the future. I am sure a few start ups will form over the questions posed in by Segaran (or maybe the questions to the questions).
Overall there were enough strengths to overcome the weak chapters. My main complaints are trivial; poor binding of the book, too many PhD candidate papers and not enough from out in the trenches. I'd love to see something from Stonebreaker here; its hard to talk about beautiful data and not have him in it. Or forget [...]and talk about many eyes. Or map reduce. Still, "Beautiful Data" succeeds. It opened up my mind to different possibilities for data representation and usage.


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In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With Beautiful Data, you will:

Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web
Learn how to visualize trends in urban crime, using maps and data mashups
Discover the challenges of designing a data processing system that works within the constraints of space travel
Learn how crowdsourcing and transparency have combined to advance the state of drug research
Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data
Learn about the massive infrastructure required to create, capture, and process DNA data

That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:
Nathan Yau Jonathan Follett and Matt Holm J.M. Hughes Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava Jeff Hammerbacher Jason Dykes and Jo Wood Jeff Jonas and Lisa Sokol Jud Valeski Alon Halevy and Jayant Madhavan Aaron Koblin with Valdean Klump Michal Migurski Jeff Heer Coco Krumme Peter Norvig Matt Wood and Ben Blackburne Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen Lukas Biewald and Brendan O'Connor Hadley Wickham, Deborah Swayne, and David Poole Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza Toby Segaran

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R Through Excel: A Spreadsheet Interface for Statistics, Data Analysis, and Graphics (Use R) Review

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(More customer reviews)Skip this paragraph if you know about R and Excel.... R is a powerful programming language that is useful for generating scientific graphics (both simple and extremely complex) and for statistical work. Unfortunately it has a steep learning curve and many say the help files are not particularly helpful for novices. Excel has a user friendly system for entering data and doing basic graphics but has relatively very limited tools for statistics or complex scientific graphics. Combining the strengths of the two is the goal of this book and the free software that goes with it.
With the tools described in this book the user can point and click their way to common analyses and graphics inside of Excel without having to learn to write code in the R programming language. Both the software and book are good but not great because they do not add much to the existing tools for R.
Years ago, John Fox wrote a point-and-click code-generating add-on package for R called R Commander that revolutionized the usability of R. Inside of the R programming environment you can download the Rcmdr package and type library(Rcmdr) and get practically all the same functionality as the tools provided here. What the authors of this book do is bring the functionality of Rcmdr into a Excel as an add-on to the Excel 2003 menus or 2007 ribbon. The implementation is surprisingly smooth (including adding nice right-click menu items) and bug free.
The book itself is mostly nicely rendered color pictures. Think of it as a very well annotated PowerPoint presentation. You will be able to quickly page through it and it is well indexed. The less than 10 page appendix which explains how to install the R packages and required services (or how to install from scratch) is probably the most useful part of the book. The authors do not focus much on the "behind the scenes" strengths of their work which allow you to recalculate and pass information into and out of R "on the fly." However, they do include a few worksheets that demonstrate the ability to pass information into R and return graphics effortlessly. Think of this as the ability to add sliders and push buttons to Excel and have instantly updated high quality graphics. Unfortunately, they only give one example where they use there new RApply function to return a calculated value from R into Excel. There clearly is a lot of functionality but the book does not explain it.
If you are just starting with R this book will probably be a HUGE help to you because it saves you from memorizing a lot of code and it will help you learn how to write code by showing you the commands that were used to generate analyses and graphics. However, if you have experience with R and Rcmdr you probably want to save your money.

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In this book, the authors build on RExcel, a free add-in for Excel that can be downloaded from the R distribution network. RExcel seamlessly integrates the entire set of R's statistical and graphical methods into Excel, allowing students to focus on statistical methods and concepts and minimizing the distraction of learning a new programming language.

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Access Data Analysis Cookbook (Cookbooks) Review

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(More customer reviews)This book is not about designing forms, primary keys, or the use of built-in wizards to make easy queries or reports. This book is about applying Access to real-world business problems. The book addresses how to query data, how to move data to and from Access in various ways, the calculation of different financial and investment terms, and other such problems. The reader of this book should already have some Access experience and thus know how to get around the Access user interface, know basic table structures and relations among them, and how to construct simple queries. As long as you know this much or more, the book should be quite useful to anyone interested in business solutions using Access to analyze the data that is involved. The book consists of a series of recipes that provide example queries, programming tips, and also some requisite math. The following is a summary of each chapter's contents. Each section of each chapter is actually a recipe consisting of a problem - the section title - and its solution(s).
1. Query Construction - A variety of query issues are addressed, including the use of the AND, OR, IN, and NOT operators; creating union queries; and understanding join types.
1.1. Finding Unmatched Records
1.2. Making AND and OR Do What You Expect
1.3. Working with Criteria Using the IN Operator
1.4. Excluding Records with the NOT Operator
1.5. Parameterizing a Query
1.6. Returning a Top or Bottom Number of Records
1.7. Returning Distinct Records
1.8. Returning Random Records
1.9. Fine-Tuning Data Filtering with Subqueries
1.10. Combining Data with Union Queries
1.11. Inserting On-the-Fly Fields in Select Queries
1.12. Using Aliases to Simplify Your SQL Statements
1.13. Creating a Left Join
1.14. Creating a Right Join
1.15. Creating an Outer Join
2. Calculating with Queries - More on using queries to find solutions to business problems. It demonstrates how to apply aggregate functions, custom functions, regular expressions, and crosstabs.
2.1. Finding the Sum or Average in a Set of Data
2.2. Finding the Number of Items per Group
2.3. Using Expressions in Queries
2.4. Using Custom Functions in Queries
2.5. Using Regular Expressions in Queries
2.6. Using a Cartesian Product to Return All Combinations of Data
2.7. Creating a Crosstab Query to View Complex Information
3. Action Queries - How to apply queries to perform activities such as inserting, updating, and deleting data.
3.1. Running an Update Query
3.2. Appending Data
3.3. Deleting Data
3.4. Creating Tables with Make-Table Queries
4. Managing Tables, Fields, Indexes, and Queries - Introduces how to programmatically create and manipulate tables and queries.
4.1. Creating Tables Programmatically
4.2. Altering the Structure of a Table
4.3. Creating and Using an Index
4.4. Programmatically Removing a Table
4.5. Programmatically Creating a Query
5. Working with String Data - Recipes on managing text-based data. Shows how to isolate parts of a string, how to remove spaces at any place in a string, and how to manipulate numbers stored as text.
5.1. Returning Characters from the Left or Right Side of a String
5.2. Returning Characters from the Middle of a String When the Start Position and Length Are Known
5.3. Returning the Start Position of a Substring When the Characters Are Known
5.4. Stripping Spaces from the Ends of a String
5.5. Stripping Spaces from the Middle of a String
5.6. Replacing One String with Another String
5.7. Concatenating Data
5.8. Sorting Numbers That Are Stored as Text
5.9. Categorizing Characters with ASCII Codes
6. Using Programming to Manipulate Data - How to use arrays, access the Windows Registry, encrypt data, and use transaction processing. Also covered are search methods, charts, and manipulating data relationships.
6.1. Using Excel Functions from Access
6.2. Working with In-Memory Data
6.3. Working with Multidimensional Arrays
6.4. Sorting an Array
6.5. Flattening Data
6.6. Expanding Data
6.7. Encrypting Data
6.8. Applying Proximate Matching
6.9. Using Transaction Processing
6.10. Reading from and Writing to the Windows Registry
6.11. Creating Charts
6.12. Scraping Web HTML
6.13. Creating Custom Report Formatting
6.14. Rounding Values
6.15. Running Word Mail Merges
6.16. Building a Multifaceted Query Selection Screen
7. Importing and Exporting Data - Different ways of moving data into and out of Access. Covers import/ export specifications, using the FileSystemObject, XML with XSLT, and communicating with SQL Server. Exchanging data with other applications in the Office suite is also covered. Also covers how to create an RSS feed.
7.1. Creating an Import/Export Specification
7.2. Automating Imports and Exports
7.3. Exporting Data with the FileSystemObject
7.4. Importing Data with the FileSystemObject
7.5. Importing and Exporting Using XML
7.6. Generating XML Schemas
7.7. Using XSLT on Import or Export
7.8. Working with XML via the MSXML Parser
7.9. Reading and Writing XML Attributes
7.10. Creating an RSS Feed
7.11. Passing Parameters to SQL Server
7.12. Handling Returned Values from SQL Server Stored Procedures
7.13. Working with SQL Server Data Types
7.14. Handling Embedded Quotation Marks
7.15. Importing Appointments from the Outlook Calendar
7.16. Importing Emails from Outlook
7.17. Working with Outlook Contacts
7.18. Importing Data from Excel
7.19. Exporting Data to Excel
7.20. Talking to PowerPoint
7.21. Selecting Random Data
8. Date and Time Calculations - How to add time, count elapsed time, work with leap years, and manage time zones in your calculations.
8.1. Counting Elapsed Time
8.2. Counting Elapsed Time with Exceptions
8.3. Working with Time Zones
8.4. Working Around Leap Years
8.5. Isolating the Day, Month, or Year
8.6. Isolating the Hour, Minute, or Second
8.7. Adding Time
9. Business and Finance Problems - Ways of calculating depreciation, loan paybacks, and return on investment are introduced, and investment concerns such as moving averages, Head and Shoulders patterns, Bollinger Bands, and trend calculations are discussed. One recipe explains how latitude and longitude are used to determine distances between geographical areas.
9.1. Calculating Weighted Averages
9.2. Calculating a Moving Average
9.3. Calculating Payback Period
9.4. Calculating Return on Investment
9.5. Calculating Straight-Line Depreciation
9.6. Creating a Loan Payment Schedule
9.7. Using PivotTables and PivotCharts
9.8. Creating PivotTables
9.9. Charting Data
9.10. Finding Trends
9.11. Finding Head and Shoulders Patterns
9.12. Working with Bollinger Bands
9.13. Calculating Distance Between Zip Codes
Chapter 10. Statistics - The most math intensive of the chapters, it discusses statistical techniques such as frequency, variance, kurtosis, linear regression, combinations, and permutations. All the recipes here have great value in data analysis.
10.1. Creating a Histogram
10.2. Finding and Comparing the Mean, Mode, and Median
10.3. Calculating the Variance in a Set of Data
10.4. Finding the Covariance of Two Data Sets
10.5. Finding the Correlation of Two Sets of Data
10.6. Returning All Permutations in a Set of Data
10.7. Returning All Combinations in a Set of Data
10.8. Calculating the Frequency of a Value in a Set of Data
10.9. Generating Growth Rates
10.10. Determining the Probability Mass Function for a Set of Data
10.11. Computing the Kurtosis to Understand the Peakedness or Flatness of a Probability Mass Distribution
10.12. Determining the Skew of a Set of Data
10.13. Returning a Range of Data by Percentile
10.14. Determining the Rank of a Data Item
10.15. Determining the Slope and the Intercept of a Linear Regression
10.16. Measuring Volatility
One final word of advise is to purchase "Head First SQL" or some other good book on SQL if you don't already feel proficient. Although the book briefly explains each query it shows, I don't think the explanation is sufficient unless you see the stuff every day. A good thing about the book is that it shows screenshots of the application in just about every recipe and usually gives directions in clear numbered steps.

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If you have large quantities of data in a Microsoft Access database, and need to study that data in depth, this book is a data cruncher's dream. Access Data Analysis Cookbook offers practical recipes to solve a variety of common problems that users have with extracting Access data and performing calculations on it. Each recipe includes a discussion on how and why the solution works. Whether you use Access 2007 or an earlier version, this book will teach you new methods to query data, different ways to move data in and out of Access, how to calculate answers to financial and investment issues, and more. Learn how to apply statistics to summarize business information, how to jump beyond SQL by manipulating data with VBA, how to process dates and times, and even how to reach into the Excel data analysis toolkit. Recipes demonstrate ways to:

Develop basic and sophisticated queries
Apply aggregate functions, custom functions, regular expressions, and crosstabs
Apply queries to perform non-passive activities such as inserting, updating, and deleting data
Create and manipulate tables and queries programmatically
Manage text-based data, including methods to isolate parts of a string and ways to work with numbers that are stored as text
Use arrays, read and write to the Windows registry, encrypt data, and use transaction processing
Use the FileSystemObject, use XML with XSLT, communicate with SQL Server, and exchange data with other Office products
Find answers from time-based data, such as how to add time, count elapsed time, work with leap years, and how to manage time zones in your calculations
Deal with business and finance problems, including methods for calculating depreciation, loan paybacks, and Return on Investment (ROI)
Explore statistical techniques, such as frequency, variance, kurtosis, linear regression, combinations and permutations
Access Data Analysis Cookbook is a one-stop-shop for extracting nuggets of valuable information from your database, and anyone with Access experience will benefit from these tips and techniques, including seasoned developers. If you want to use your data, and not just store it, you'll find this guide indispensable.

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R in a Nutshell: A Desktop Quick Reference Review

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(More customer reviews)'R in a Nutshell' is the essential introductory book on R. Do not try to learn R without it.
I made two attempts to learn R before purchasing this book. In both previous attempts, I had to abort and use another tool to solve my problem because it was taking me too long to accomplish very simple things in R.
The reason R is hard to learn is that its documentation is organized for statisticians that already know R, but have forgotten a detail or two. There are a few other books on learning R, but they are setup like a college course - complete the entire book and THEN you can actually accomplish something.
R in a Nutshell allows you to get working immediately. Simply lookup what you need to do. The firsts thing I did was load a file and make a histogram. I found that stuff in the section on "Loading Data" and the section on charts. In no time I was making stacked area charts for cohorts. Now R is an essential tool for me - and I haven't even taken the time to learn it well! With this book, I don't have to. I can learn as I go. So I actually use R.
Do not R without it.

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What people are saying about R in a Nutshell

"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians." --Martin Schultz, Arthur K. Watson Professor of Computer Science, Yale University

"R in a Nutshell is an ideal book for getting started with R. Newcomers will find the fundamentals for performing statistical analysis and graphics, all illustrated with practical examples. This book is an invaluable reference for anyone who wants to learn what R is and what is can do, even for longtime R users looking for new tips and tricks." --David M. Smith, Editor of the "Revolutions" blog at REvolution Computing

Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.

The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.

Understand the basics of the language, including the nature of R objects
Learn how to write R functions and build your own packages
Work with data through visualization, statistical analysis, and other methods
Explore the wealth of packages contributed by the R community
Become familiar with the lattice graphics package for high-level data visualization
Learn about bioinformatics packages provided by Bioconductor

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Visualizing Data: Exploring and Explaining Data with the Processing Environment Review

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(More customer reviews)This book allowed me to quickly create some simple applications using the processing API. So, in that respect, the book was successful. However, the book falls short in three respects.
1) One would expect a book with the title "Visualizing Data" to be crammed with pictures showing many different data visualizations. However, this book has relatively few. Every colleague of mine who passed by my desk and picked up the book had the exact same reaction.
2) The processing language is touted as a means for people unfamiliar with programming to get up to speed with visualization. However, I would be very surprised if anyone with little programming experience would get much out of this book.
3) Don't expect to use this book as a reference for the processing language. It is basically just a collection of half explained examples. Consider for example the function smooth(). This function appears in almost every example but forget about trying to find an explanation of what the function does in the book.
The book is probably worth buying to get up to speed quickly but plan on spending a significant amount of time sifting through the processing.org website and other online resources before being able to get anything non-trivial done. And if you don't already know Java then don't expect to accomplish anything even modestly complex without a lot of outside help.


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Enormous quantities of data go unused or underused today, simply because people can't visualize the quantities and relationships in it. Using a downloadable programming environment developed by the author, Visualizing Data demonstrates methods for representing data accurately on the Web and elsewhere, complete with user interaction, animation, and more. How do the 3.1 billion A, C, G and T letters of the human genome compare to those of a chimp or a mouse? What do the paths that millions of visitors take through a web site look like? With Visualizing Data, you learn how to answer complex questions like these with thoroughly interactive displays. We're not talking about cookie-cutter charts and graphs. This book teaches you how to design entire interfaces around large, complex data sets with the help of a powerful new design and prototyping tool called "Processing". Used by many researchers and companies to convey specific data in a clear and understandable manner, the Processing beta is available free. With this tool and Visualizing Data as a guide, you'll learn basic visualization principles, how to choose the right kind of display for your purposes, and how to provide interactive features that will bring users to your site over and over. This book teaches you:

The seven stages of visualizing data -- acquire, parse, filter, mine, represent, refine, and interact
How all data problems begin with a question and end with a narrative construct that provides a clear answer without extraneous details
Several example projects with the code to make them work
Positive and negative points of each representation discussed. The focus is on customization so that each one best suits what you want to convey about your data set
The book does not provide ready-made "visualizations" that can be plugged into any data set. Instead, with chapters divided by types of data rather than types of display, you'll learn how each visualization conveys the unique properties of the data it represents -- why the data was collected, what's interesting about it, and what stories it can tell. Visualizing Data teaches you how to answer questions, not simply display information.

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Interactive Data Visualization: Foundations, Techniques, and Applications Review

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(More customer reviews)Had to get this overpriced book for an Info Viz class. Be aware that this book is much more of a technical book than a design book. There's a ton of information contained in here, but I also found a surprising amount of quality issues.
First, the flow of the book seems completely off, diving into highly technical material in the second chapter, then pulling back into high level concepts in later chapters. Also, many of the images are not of the quality I would expect from a text book. Many are blurry or scaled inappropriately, given the amount of detail they contain. Finally, there are some glaring mistakes in the copy. For instance, at the end of one section of the book, placeholder notes from the authors of what should be written is included instead of the actual final copy! Where's the editor? Was it rushed to print?
Given the price, I expected a much higher level of quality. Despite the problems listed above, the text could be useful resource for anyone interested in the nuts and bolts of data visualization.

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This book provides the theory, practical details, and tools necessary for building visualizations or systems involving the visualization of data. The authors cover the spectrum of data visualizations, including mathematical and statistical graphs, cartography for displaying geographic information, two- and three-dimensional scientific displays, integrated analysis and visualization tools, and general information visualization techniques. Practitioners, developers, teachers and students as well as those interested in gaining some exposure to the field will get an in-depth understanding of visualization techniques and are provided with sufficient information, often with full source code, to complete an implementation; those with more modest aspirations can focus on the concepts, theory and high-level algorithm details.

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Visualizing Data Review

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(More customer reviews)This book was recommended highly to me by a former university professor (and now consultant). It exceeds my expectations. The figures and acompanying explanations are very clear, as is the language throughout. Visualizing Data discusses several tools with which I was not familiar, and clarifies tools that I thought I understood (including box plots). I have taken several university statistics classes, but I believe this book would help anyone involved in displaying or interpreting data. A picture may be worth a thousand words, but when your business depends on it, a well-defined plot or graph can be worth much more. Visualizing Data enables you to produce well-defined plots and graphs with confidence.

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Visualizing Data is about visualizationtools that provide deep insight into thestructure of data. There are graphicaltools such as coplots, multiway dot plots,and the equal count algorithm. There arefitting tools such as loess and bisquarethat fit equations, nonparametric curves,and nonparametric surfaces to data.But the book is much more than just acompendium of useful tools. It conveys astrategy for data analysis that stressesthe use of visualization to thoroughlystudy the structure of data and to checkthe validity of statistical models fittedto data. The result of the tools and thestrategy is a vast increase in what you canlearn from your data. The book demonstratesthis by reanalyzing many data sets from thescientific literature, revealing missedeffects and inappropriate models fitted to data.

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Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics) Review

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(More customer reviews)Back in 2000 Stephen Fienberg gave a talk at the University of California at Irvine on the 2000 census and his book "Who Counts". After the talk I went to dinner with him, my colleague Bob Newcomb and Anita Iannucci. Driving to dinner Bob ask Steve for a recommendation on a multivariate textbook. A number of choice were mentioned. Bob's favorite was Cooley and Lohnes but that was a bit dated. He was definitely looking for an applied text and not a theoretical one. I learned my multivariate analysis out of the first edition of Ted Anderson's book. But that is traditional multivariate Gaussian theory and is not at all an applied text. I always liked Gnanadesikan's book and I mentioned that. Srivastava and carter is an applied text that I like and there are many other choices.
I don't recall many of Fienberg's suggestions but I do distinctly recall that he did say that now you can teach it as a special case of the generalized linear models. The idea seemed to make sense to me but I couldn't picture the details. This book is apparently the book Fienberg had in mind. He might have been thinking about the first edition because this second edition was not out then.
The book is very applied and modern and covers many important topics for biostatisticians. Coverage includes multicategorical responses, semi and nonparametric modelling, time series and longitudinal data, random effects models, state space models including Kalman Filters and nonlinear models, and survival analysis. This is not traditional multivariate data but covers many type of multivariate data and models that do not fit the standard multivariate Gaussian theory.
Chapter 4 on selecting and checking models seems to deal with the classical linear models taking a non-standard approach through the methods of generalized linear models.
Excellent text for an applied course and for a reference book. It also covers hidden Markov models and Bayesian methods (including the MCMC implementation and the WinBugs software).


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The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.

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Bayesian Adaptive Methods for Clinical Trials (Chapman & Hall/CRC Biostatistics Series) Review

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(More customer reviews)In the pharmaceutical industry adaptive designs are currently the rage because of their many potential advantages due to their flexibility. It allows you to stop early for efficacy or futility. It can do drug dose selection more easily and may have patients on inferior treatment for smaller amounts of time. There have already been four or five books published from the frequentist point of view. This is the first serious text on adaptive designs using the Bayesian approach. Pharmaceutical companies including Johnson and Johnson, Eli Lilly, Pfizer, Merck, Novartis, Novo Nordisk, Millennium, AMAG and GlaxoSmithKline have all been successful at running adaptive trials. Merck for example has already completed more than 40 adaptive design trials. Such trials can be done in phase II, phase III or a combining of phases II and III in a single adaptive trial. Merck claims to have completed over 40 adaptive trials. The M D Anderson Medical Center at UT Houston runs hundreds of adaptive trials (all as far as I know using the Bayesian methodology). Don Berry runs the biostatistics group at M D Anderson and he and his son scott own a consulting group that helps companies run Bayesian adaptive designs. Eli Lilly has been one of their clients on a drug trial and Biosense Webster, a J& J company, used them for a Bayesian trial on one of their ablation catheters. Scott Berry isone of the authors of this book and a lot of the book is devoted to work of Berry first at Duke and then later at M D Anderson and Berry Consultants.
Adaptive designs have logistic problems but companies have been able to overcome the problems motivated by the overall time and money saving benefits. All types of studies are illustrated from phase I through phase III and the examples are real and practical. Even when taking the Bayesian approach issues of frequentist properties for the designs comes up. Missing data, multiple testing, type I error and power of the test conditional and unconidtional are important when the frequentist approach is applied. The authors admit that both frequentist and Bayesian properties for a design are important and can be evaluated through simulation.
Although adaptive designs can be implemented effectively using either the Bayesian or the frequentist approaches. But Bayesian trials are a little more natural and simpler. This is the right book to get if you are interested in Bayesian methods.

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Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis.The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on late (Phase III) studies, the authors emphasize modern adaptive methods and seamless Phase II-III trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis.For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits. The authors provide many detailed examples drawing on real data sets. The R and WinBUGS codes used throughout are available on supporting websites.

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A Primer of Ecology with R (Use R) Review

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(More customer reviews)This primer provides an excellent, upper-level, introduction to theoretical and simulation ecology. Working through the presented R code and exercises provides a deeper understanding of the thinking of many of the most famous theoretical ecologists, while also introducing the methods by which students can examine ecological questions through simulation.

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Provides simple explanations of the important concepts in population and community ecology.Provides R code throughout, to illustrate model development and analysis, as well as appendix introducing the R language.Interweaves ecological content and code so that either stands alone. Supplemental web site for additional code.

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Structural Equations with Latent Variables Review

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(More customer reviews)The software Lisrel was developed to model and analyze data using structural equation models which involve the introduction of latent variables. Although this topic has historically been most commonly used in the social sciences including psychology and sociology, it is finding a wide range of applications as statisticians encounter more and more problems where it is appropriate to use latent variables.
Bollen provides a thorough treatment of the topic that has advanced some since the publication of the book . This is still the best source for a detailed account of the methods. Bengt Meuthen at UCLA was one of the pioneers of the methodology and his books and papers provide good additional sources for the reader who wants to understand the theory and the software tools.

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Analysis of Ordinal Categorical Data Alan Agresti Statistical Science Now has its first coordinated manual of methods for analyzing ordered categorical data. This book discusses specialized models that, unlike standard methods underlying nominal categorical data, efficiently use the information on ordering. It begins with an introduction to basic descriptive and inferential methods for categorical data, and then gives thorough coverage of the most current developments, such as loglinear and logit models for ordinal data. Special emphasis is placed on interpretation and application of methods and contains an integrated comparison of the available strategies for analyzing ordinal data. This is a case study work with illuminating examples taken from across the wide spectrum of ordinal categorical applications. 1984 (0 471-89055-3) 287 pp. Regression Diagnostics Identifying Influential Data and Sources of Collinearity David A. Belsley, Edwin Kuh and Roy E. Welsch This book provides the practicing statistician and econometrician with new tools for assessing the quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are either unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data and help to identify the variables involved in each; and pinpoint the estimated coefficients that are potentially most adversely affected. The primary emphasis of these contributions is on diagnostics, but suggestions for remedial action are given and illustrated. 1980 (0 471-05856-4) 292 pp. Applied Regression Analysis Second Edition Norman Draper and Harry Smith Featuring a significant expansion of material reflecting recent advances, here is a complete and up-to-date introduction to the fundamentals of regression analysis, focusing on understanding the latest concepts and applications of these methods. The authors thoroughly explore the fitting and checking of both linear and nonlinear regression models, using small or large data sets and pocket or high-speed computing equipment. Features added to this Second Edition include the practical implications of linear regression; the Durbin-Watson test for serial correlation; families of transformations; inverse, ridge, latent root and robust regression; and nonlinear growth models. Includes many new exercises and worked examples. 1981 (0 471-02995-5) 709 pp.

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Risk Analysis of Complex and Uncertain Systems (International Series in Operations Research & Management Science) Review

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(More customer reviews)This is an excellent, approachable read for any risk manager; understanding its examples requires only elementary probability, statistics and calculus, though the foundations are much deeper. The author uses direct language, and does not hesitate to declare a fashionable risk analysis technique "worse than useless." The author shows how not to do risk analysis, using simple but devastating examples to illustrate the weaknesses of prioritized investments, subject matter expert opinion, risk matrices and qualitative risk assessments, and the independence assumption. Then, case studies present constructive examples of good practice. Refreshingly, this text clearly distinguishes between threats from Mother Nature, and those posed by an intelligent adversary. There is unevenness, because this is an edited ensemble of papers originally published in a variety of technical journals; however, this is also a strength, because the appeal and scholarship underlying biological, engineering, and social science examples is broad. This is not a how-to guide, and won't help fill in a blank page risk analysis; however, this is an excellent source for the skeptical consumer of contemporary risk management advice and products, and hopefully will have some influence with policy makers who are the source of simplistic and dangerous guidance.

Gerald G. Brown
Distinguished Professor of Operations Research
Naval Postgraduate School
National Academy of Engineering
INFORMS Fellow


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In Risk Analysis of Complex and Uncertain Systems acknowledged risk authority Tony Cox shows all risk practitioners how Quantitative Risk Assessment (QRA) can be used to improve risk management decisions and policies. It develops and illustrates QRA methods for complex and uncertain biological, engineering, and social systems - systems that have behaviors that are just too complex to be modeled accurately in detail with high confidence - and shows how they can be applied to applications including assessing and managing risks from chemical carcinogens, antibiotic resistance, mad cow disease, terrorist attacks, and accidental or deliberate failures in telecommunications network infrastructure. This book was written for a broad range of practitioners, including decision risk analysts, operations researchers and management scientists, quantitative policy analysts, economists, health and safety risk assessors, engineers, and modelers.

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Analyzing Business Data with Excel Review

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(More customer reviews)I like the viewpoint of this book; I have a business problem, now how do I attack that in Excel? So many books come at business problems from the perspective of a specific feature in the product. This book teaches the solution to a complex business data analysis problem through use of the features in Excel. Excellent!
It's a small book. The text is a little terse, but that's ok. Screenshots are used sparingly.
If what you want is a feature by feature breakdown of Excel then this book isn't for you. But if what you have is some data that you need to crunch and you don't know much about Excel then check this book out.

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As one of the most widely used desktop applications ever created, Excel is familiar to just about everyone with a computer and a keyboard.Yet most of us don't know the full extent of what Excel can do, mostly because of its recent growth in power, versatility, and complexity.The truth is that there are many ways Excel can help make your job easier-beyond calculating sums and averages in a standard spreadsheet.

Analyzing Business Data with Excel shows you how to solve real-world business problems by taking Excel's data analysis features to the max.Rather than focusing on individual Excel functions and features, the book keys directly on the needs of business users.Most of the chapters start with a business problem or question, and then show you how to create pointed spreadsheets that address common data analysis issues.

Aimed primarily at experienced Excel users, the book doesn't spend much time on the basics.After introducing some necessary general tools, it quickly moves into more specific problem areas, such as the following:

Statistics
Pivot tables
Workload forecasting
Modeling
Measuring quality
Monitoring complex systems
Queuing
Optimizing
Importing data

If you feel as though you're getting shortchanged by your overall application of Excel, Analyzing Business Data with Excel is just the antidote.It addresses the growing Excel data analysis market head on.Accountants, managers, analysts, engineers, and supervisors-one and all-will learn how to turn Excel functionality into actual solutions for the business problems that confront them.

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Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis Review

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(More customer reviews)I read this book's earlier edition so that I know this book is a good one. I bought a kindle version of it and found that the 4th edition is meeting my expectation. Only troubling issue that I found from this kindle version is that the data cd coming with a paper version of this book is missing in kindle version. Neither the publisher nor the amazon.com provide a link to the data cd. My complain to amazon.com is that they didn't inform their customer about this.

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This book introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. A few sections of the book make use of elementary matrix algebra. An appendix on the topic is provided for those who need a review. The author maintains an informal style so as to increase the book's accessibility. Notes at the end of each chapter provide some of the more technical details. The book is not tied to a particular computer program, but special attention is paid to LISREL, EQS, AMOS, and Mx. New in the fourth edition of Latent Variable Models: *a data CD that features the correlation and covariance matrices used in the exercises; *new sections on missing data, non-normality, mediation, factorial invariance, and automating the construction of path diagrams; and *reorganization of chapters 3-7 to enhance the flow of the book and its flexibility for teaching. Intended for advanced students and researchers in the areas of social, educational, clinical, industrial, consumer, personality, and developmental psychology, sociology, political science, and marketing, some prior familiarity with correlation and regression is helpful.

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Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions Review

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(More customer reviews)This book is for professionals that must analyze data in their daily work. First off, if you are unfamiliar with the approach of the "Head First" series of books by O'Reilly, the approach was and is revolutionary in the field of technical writing. The authors of this series know that page after page of terse text will not easily penetrate the brain of the working professional who needs help rather quickly. Traditional textbook models work best on students in a traditional classroom setting who can slowly absorb material over a period of several months with the help of bi-weekly classroom sessions with a professor. The working professional does not have this luxury of time or of personal tutoring.
Thus the authors both penetrate your brain and hold your interest by serving information up in unusual ways - odd pictures and illustrations, Q&A sessions, repeating the same material in different ways, and interesting case studies in which you are asked at every step to give your input. They'll even lead you down the the wrong path every now and then so that you remember the right one all the better.
As for the subject matter, this is not a book on statistics and how to solve problems in statistics. Instead, it is how you use various statistical models and tools and visualization to analyze often confusing corporate data and come up with recommendations based on that data. Some mathematical methods will be presented as they are necessary to solving the underlying problems - optimization, hypothesis testing, bayesian statistics, subjective probabilities, heuristics, and histograms - these are all mentioned and even have their own chapters. However, this book is also about tools - R and the analysis tools of Excel specifically. In the appendix, this book even shows you how to install R.
However, I don't believe that you could get away with knowing nothing of statistics and really get the most out of this book. If you do happen to have the luxury of a little time I suggest the following. Read the excellent Head First Statistics as a tutorial, and then use the problems in Schaum's Outline of Statistics (Schaum's Outline Series) to test your knowledge. Then you should be more than ready for this book.
The author has a chapter entitled "leftovers" that tells you what this book does not cover. I include that here so that you don't waste your time if this is what you are looking for:
1 Everything else in statistics
2 Excel skills - (book assumes previous experience)
3 Edward Tufte and his principles of visualization
4 PivotTables
5 Nonlinear and multiple regression
7 Null-alternative hypothesis testing
8 Randomness
9 Google Docs
I highly recommend this book for the right audience with the right experience level.

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Today, interpreting data is a critical decision-making factor for businesses and organizations. If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to:



Determine which data sources to use for collecting information
Assess data quality and distinguish signal from noise
Build basic data models to illuminate patterns, and assimilate new information into the models
Cope with ambiguous information
Design experiments to test hypotheses and draw conclusions
Use segmentation to organize your data within discrete market groups
Visualize data distributions to reveal new relationships and persuade others
Predict the future with sampling and probability models
Clean your data to make it useful
Communicate the results of your analysis to your audience

Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

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Statistical Modeling: A Fresh Approach Review

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(More customer reviews)I have a couple of shelves full of introductory statistics books, but for various reasons none have ever seemed suitable for my undergraduate biology/ecology students. Daniel Kaplan has now given me a book I can confidently recommend.
This book, as the title suggest, focusses on a modeling/regression approach but still includes an appropriate discussion on null hypothesis testing and anova. This discussion comes near the end of the book once the modeling approach is clearly understood. It could easily be used as a first statistics book (which is how I would use it), but it could also follow a more traditional null hypothesis based first course.
The writing style is clear and easily followed, with just enough explanation to understand why something is being done, but without the mathematical notation that scares so many students away. A geometrical approach is taken to explain some of the more theoretical aspects. It's also just long enough to be useful, without intimidating students with an enormous number of pages. Chapters are generally short and there is a nice logical flow as you work through the book. The practical examples use R, are well thought out, and great care is taken to explain the output.
Probably not the book for an undergraduate statistician, but for other disciplines that need a good understanding of not only statistical practice, but also statistical thinking, I have yet to come across anything better. Indeed I am so impressed with this book that I felt compelled to write my first ever Amazon review.

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"Statistical Modeling: A Fresh Approach" introduces and illuminates the statistical reasoning used in modern research throughout the natural and social sciences, medicine, government, and commerce.It emphasizes the use of models to untangle and quantify variation in observed data.By a deft and concise use of computing coupled with an innovative geometrical presentation of the relationship among variables,"A Fresh Approach" reveals the logic of statistical inference and empowers the reader to use and understand techniques such as analysis of covariance that are widely used in published research but hardly ever found in introductory texts.Recognizing the essential role the computer plays in modern statistics,{\em A Fresh Approach}provides a complete and self-contained introduction to statistical computing using the powerful (and free) statistics package R.Exercises, software and datasets for the book are available at www.macalester.edu/~kaplan/ISM.

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