Kategoria BISAC: Business & Economics >> Statistics

Updated version featuring all new material.

If you have ever looked for P-values by shopping at P mart, tried to watch the Bernoulli Trails on "People's Court," or think that the standard deviation is a criminal offense in six states, then you need The Cartoon Guide to Statistics to put you on the road to statistical literacy.

The Cartoon Guide to Statistics covers all the central ideas of modern statistics: the summary and display of data, probability in gambling and medicine, random variables, Bernoulli Trails, the Central Limit Theorem, hypothesis testing,...

Ready to implement better, faster, cheaper, more-profitable processes in your organization? Lean Six Sigma Demystified, Second Edition, shows you how to use proven techniques for simplifying, streamlining, and optimizing business practices for maximum productivity and profitability.

Written in a step-by-step format, this practical guide covers the fundamental methods and tools of Lean Six Sigma. You'll get details on reducing defects and deviation, sustaining improvements, and achieving laser-focused process innovations. Measurement...

Each entry gives an introduction to the topic, lists the key relevant features, gives a worked example, a concise summary and a selection of further reading suggestions.

It is suitable for researchers and academics who need a handy and quick point of reference.

R is both an object-oriented language and a functional language that is easy to learn, easy to use, and...

Distribution-free resampling methods--permutation tests, decision trees, and the bootstrap--are used today in virtually every research area. A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the precision of sample-based estimates and to determine sample size, data permutations to test hypotheses, and the readily-interpreted decision tree to replace arcane regression methods.

Highlights

• Each chapter contains dozens of thought provoking questions,...

Discover Novel and Insightful Knowledge from Data Represented as a Graph
Practical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new...