Statistical Data Analytics
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Introduction to Statistics and Data Analysis
Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.
Statistical Data Analysis Handbook
It must be emphasized that this is not a text book on statistics. Instead it is a working tool that presents data analysis in clear, concise terms which can be readily understood even by those without formal training in statistics. Following pivotal chapters on the art of analysis, you will find two invaluable reference sections to which you're bound to turn again and again as you make your analyses. The first section gives you the data analyst's-as opposed to the statistician's-eyeview of the basics of a number of fundamental statistical techniques, including random samples, p-values, the analysis of variance, statistical tests, and testing. The second gives you 20 statistical tables that have been especially formatted and expanded to make them far easier to read and apply than conventional statistical tables.
Statistical Data Analytics
Author: Walter W. Piegorsch
language: en
Publisher: John Wiley & Sons
Release Date: 2016-03-22
Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery. Extensive solutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.