Understanding Robust and Exploratory Data Analysis

This book PDF is perfect for those who love Mathematics genre, written by David C. Hoaglin and published by Unknown which was released on 15 June 1983 with total hardcover pages 472. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Understanding Robust and Exploratory Data Analysis books below.

Understanding Robust and Exploratory Data Analysis

Author	: David C. Hoaglin
File Size	: 51,7 Mb
Publisher	: Unknown
Language	: English
Release Date	: 15 June 1983
ISBN	: UOM:39015015726261
Pages	: 472 pages

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Understanding Robust and Exploratory Data Analysis by David C. Hoaglin Book PDF Summary

Textbook on robust and exploratory data analysis and related statistical methods - covers stem-and-leaf displays, letter values, boxplots and batch graphic displays, resistant lines, analysis of two- way tables by medians, examining residuals, mathematical aspects of transformation, scale estimators, comparison of location estimators, confidence intervals for location, etc. References.

Understanding Robust and Exploratory Data Analysis by David C. Hoaglin,Frederick Mosteller,John W. Tukey

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Understanding Robust and Exploratory Data Analysis by David C. Hoaglin,Frederick Mosteller,John W. Tukey

Originally published in hardcover in 1982, this book is now offered in a Wiley Classics Library edition. A contributed volume, edited by some of the preeminent statisticians of the 20th century, Understanding of Robust and Exploratory Data Analysis explains why and how to use exploratory data analysis and robust and resistant

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Exploring Data Tables Trends and Shapes by David C. Hoaglin,Frederick Mosteller,John W. Tukey

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future

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Fundamentals of Exploratory Analysis of Variance by David C. Hoaglin,Frederick Mosteller,John W. Tukey

The analysis of variance is presented as an exploratory component of data analysis, while retaining the customary least squares fitting methods. Balanced data layouts are used to reveal key ideas and techniques for exploration. The approach emphasizes both the individual observations and the separate parts that the analysis produces. Most

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Practical Statistics for Data Scientists by Peter Bruce,Andrew Bruce

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you

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Secondary Analysis of Electronic Health Records by MIT Critical Data

This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both

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Spatial Data Analysis in the Social and Environmental Sciences by Robert P. Haining,Robert Haining

Within both the social and environmental sciences, much of the data collected is within a spatial context and requires statistical analysis for interpretation. The purpose of this book is to describe current methods for the analysis of spatial data. Methods described include data description, map interpolation, and exploratory and explanatory

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Robust Correlation by Georgy L. Shevlyakov,Hannu Oja

This bookpresents material on both the analysis of the classical concepts of correlation and on the development of their robust versions, as well as discussing the related concepts of correlation matrices, partial correlation, canonical correlation, rank correlations, with the corresponding robust and non-robust estimation procedures. Every chapter contains a set

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