Learn IT - Books tags statistical

Bayesian Biostatistics and Diagnostic Medicine

CRC Press, 2007

Bayesian methods are being used more often than ever before in biology and medicine. For example, at the University of Texas MD Anderson Cancer Center, Bayesian sequential stopping rules routinely are used for the design of clinical trials. This book is based on the author’s experience working with a variety of...

Robust Statistical Procedures (CBMS-NSF Regional Conference Series in Applied Mathematics)

SIAM, 1987

When SIAM contacted me about the preparation of a second edition of this booklet, it became clear almost immediately that any attempt to rewrite the booklet would invite the danger of spoiling its purpose. The booklet had served me well as a skeleton and draft outline for my subsequent book Robust Statistics (Wiley, 1981). I...

Pancreatic Cancer: Methods and Protocols (Methods in Molecular Medicine)

Humana Press, 2004

Pancreatic ductal adenocarcinoma is the fourth leading cause of cancer death in the United States. Annually approximately 30,000 Americans are diagnosed with the disease and most will die from it within five years. Pancreatic ductal adenocarcinoma is unique because of its late onset in age, high mortality, small tumor samples...

Computational Molecular Dynamics: Challenges, Methods, Ideas: Proceeding of the 2nd International Symposium

Springer, 1998

On May 21-24, 1997 the Second International Symposium on Algorithms for Macromolecular Modelling was held at the Konrad Zuse Zentrum in Berlin. The event brought together computational scientists in fields like biochemistry, biophysics, physical chemistry, or statistical physics and numerical analysts as well as computer scientists working on...

Bayesian Models for Categorical Data (Wiley Series in Probability and Statistics)

John Wiley & Sons, 2005

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical...

R Graphics Cookbook

O'Reilly, 2013

I started using R several years ago to analyze data I had collected for my research in graduate school. My motivation at first was to escape from the restrictive environments and canned analyses offered by statistical programs like SPSS. And even better, because it’s freely available, I didn’t need to...

Java for Data Science

Packt Publishing, 2017

Examine the techniques and Java tools supporting the growing field of data science About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples Make your Java applications more capable using machine learning Who This Book Is...

Beginning SQL Server R Services: Analytics for Data Scientists

Andrei Besedin, 2016

Learn how to develop powerful data analytics applications quickly for SQL Server database administrators and developers. Organizations will be able to sift data and derive the business intelligence needed to drive business decisions and profit. The addition of R to SQL Server 2016 places a powerful analytical processor into an...

Visual Six Sigma: Making Data Analysis Lean (Wiley and SAS Business Series)

John Wiley & Sons, 2016

Streamline data analysis with an intuitive, visual Six Sigma strategy

Visual Six Sigma provides the statistical techniques that help you get more information from your data. A unique emphasis on the visual allows you to take a more active role in data-driven decision making, so you can leverage your contextual...

Python Data Science Handbook: Essential Tools for Working with Data

O'Reilly, 2016

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib,...

An R and S-Plus® Companion to Multivariate Analysis (Springer Texts in Statistics)

Springer, 2007

Applied statisticians often need to perform analyses of multivariate data; for these they will typically use one of the statistical software packages, S-Plus or R. This book sets out how to use these packages for these analyses in a concise and easy-to-use way, and will save users having to buy two books for the job. The author is...

Data Mining Using SAS Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Springer, 2002

Most books on data mining focus on principles and furnish few instructions on how to carry out a data mining project. Data Mining Using SAS Applications not only introduces the key concepts but also enables readers to understand and successfully apply data mining methods using powerful yet user-friendly SAS macro-call files. These methods...

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