Graphical Methods for Data Analysis
This book present graphical methods for analysing data. Some methods are new and some are old, some require a computer and others only paper and pencil; but they are all powerful data analysis tools. In many situations, a set of data even a large set- can be adequately analysed through graphical methods alone. In most other situations, a few well-chosen graphical displays can significantly enhance numerical statistical analyses.
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Graphical Methods for Data Analysis
This book present graphical methods for analysing data. Some methods are new and some are old, some require a computer and others only paper and pencil; but they are all powerful data analysis tools. In many situations, a set of data even a large set- can be adequately analysed through graphical methods alone. In most other situations, a few well-chosen graphical displays can significantly enhance numerical statistical analyses.
210.49 In Stock
Graphical Methods for Data Analysis

Graphical Methods for Data Analysis

by J. M. Chambers
Graphical Methods for Data Analysis

Graphical Methods for Data Analysis

by J. M. Chambers

eBook

$210.49  $280.00 Save 25% Current price is $210.49, Original price is $280. You Save 25%.

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Overview

This book present graphical methods for analysing data. Some methods are new and some are old, some require a computer and others only paper and pencil; but they are all powerful data analysis tools. In many situations, a set of data even a large set- can be adequately analysed through graphical methods alone. In most other situations, a few well-chosen graphical displays can significantly enhance numerical statistical analyses.

Product Details

ISBN-13: 9781351089203
Publisher: CRC Press
Publication date: 01/18/2018
Sold by: Barnes & Noble
Format: eBook
Pages: 410
File size: 21 MB
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About the Author

J. M. Chambers

Table of Contents

1. Portraying the Distribution of a Set of Data 2. Comparing Data Distributions 3. Studying Two-Dimensional Data 4. Plotting Multivariate Data 5. Assessing Distributional Assumptions About Data 6. Developing and Accessing Regression Models 7. General Principles and Techniques
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