R for Microsoft Excel Users: Making the Transition for Statistical Analysis

Carlberg, Conrad

ISBN 10: 0789757850 / ISBN 13: 9780789757852

Published by Que, 2016

About this Item

Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.05.

Seller Inventory # G0789757850I4N10

Bibliographic Details

• Title: R for Microsoft Excel Users: Making the Transition for Statistical Analysis
• Publisher: Que
• Publication Date: 2016
• Language: English
• ISBN 10: 0789757850
• ISBN 13: 9780789757852
• Binding: Paperback
• Condition: Very Good
• Dust Jacket Condition: No Jacket

About this title

Synopsis

 Microsoft Excel can perform many statistical analyses, but thousands of business users and analysts are now reaching its limits. R, in contrast, can perform virtually any imaginable analysis―if you can get over its learning curve. In R for Microsoft® Excel Users, Conrad Carlberg shows exactly how to get the most from both programs.

 

Drawing on his immense experience helping organizations apply statistical methods, Carlberg reviews how to perform key tasks in Excel, and then guides you through reaching the same outcome in R―including which packages to install and how to access them. Carlberg offers expert advice on when and how to use Excel, when and how to use R instead, and the strengths and weaknesses of each tool.

 

Writing in clear, understandable English, Carlberg combines essential statistical theory with hands-on examples reflecting real-world challenges. By the time you’ve finished, you’ll be comfortable using R to solve a wide spectrum of problems―including many you just couldn’t handle with Excel.

 

• Smoothly transition to R and its radically different user interface

• Leverage the R community’s immense library of packages

• Efficiently move data between Excel and R

• Use R’s DescTools for descriptive statistics, including bivariate analyses

• Perform regression analysis and statistical inference in R and Excel

• Analyze variance and covariance, including single-factor and factorial ANOVA

• Use R’s mlogit package and glm function for Solver-style logistic regression

• Analyze time series and principal components with R and Excel

About the Author

Conrad Carlberg (www.conradcarlberg.com) is a nationally recognized expert on quantitative analysis and on data analysis and management applications such as Microsoft Excel, SAS, and Oracle. He holds a Ph.D. in statistics from the University of Colorado and is a many-time recipient of Microsoft's Excel MVP designation.

 

Carlberg is a Southern California native. After college he moved to Colorado, where he worked for a succession of startups and attended graduate school. He spent two years in the Middle East, teaching computer science and dodging surly camels. After finishing graduate school, Carlberg worked at US West (a Baby Bell) in product management and at Motorola.

 

In 1995 he started a small consulting business which provides design and analysis services to companies that want to guide their business decisions by means of quantitative analysis–approaches that today we group under the term "analytics." He enjoys writing about those techniques and, in particular, how to carry them out using the world's most popular numeric analysis application, Microsoft Excel.