Making Sense of Data I E-Book

Glenn J. Myatt

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 308

Veröffentlichungsjahr: 2014

Beschreibung

Praise for the First Edition

 “...a well-written book on data analysis and data mining that provides an excellent foundation...”

—CHOICE

“This is a must-read book for learning practical statistics and data analysis...”

—Computing Reviews.com

 

A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study.

In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features:

• Updated exercises for both manual and computer-aided implementation with accompanying worked examples
• New appendices with coverage on the freely available Traceis™ software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance
• New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches
• Additional real-world examples of data preparation to establish a practical background for making decisions from data

Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

Leseprobe

MAKING SENSE OF DATA I

A Practical Guide to Exploratory Data Analysis and Data Mining

Second Edition

GLENN J. MYATT WAYNE P. JOHNSON

Copyright © 2014 by John Wiley & Sons, Inc. All rights reserved

Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada

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Library of Congress Cataloging-in-Publication Data:

Myatt, Glenn J., 1969–   [Making sense of data]   Making sense of data I : a practical guide to exploratory data analysis and data mining / Glenn J. Myatt, Wayne P. Johnson. – Second edition.       pages cm   Revised edition of: Making sense of data. c2007.   Includes bibliographical references and index.   ISBN 978-1-118-40741-7 (paper)  1. Data mining.   2. Mathematical statistics.   I. Johnson, Wayne P.   II. Title.   QA276.M92 2014   006.3′12–dc23

 2014007303

ISBN: 9781118407417

CONTENTS

PREFACE

1 INTRODUCTION

1.1 Overview

1.2 Sources of Data

1.3 Process for Making Sense of Data

1.4 Overview of Book

1.5 Summary

Further Reading

2 DESCRIBING DATA

2.1 Overview

2.2 Observations and Variables

2.3 Types of Variables

2.4 Central Tendency

2.5 Distribution of the Data

2.6 Confidence Intervals

2.7 Hypothesis Tests

Exercises

Further Reading

3 PREPARING DATA TABLES

3.1 Overview

3.2 Cleaning the Data

3.3 Removing Observations and Variables

3.4 Generating Consistent Scales Across Variables

3.5 New Frequency Distribution

3.6 Converting Text to Numbers

3.7 Converting Continuous Data to Categories

3.8 Combining Variables

3.9 Generating Groups

3.10 Preparing Unstructured Data

Exercises

Further Reading

4 UNDERSTANDING RELATIONSHIPS

4.1 Overview

4.2 Visualizing Relationships Between Variables

4.3 Calculating Metrics About Relationships

Exercises

Further Reading

5 IDENTIFYING AND UNDERSTANDING GROUPS

5.1 Overview

5.2 Clustering

5.3 Association Rules

5.4 Learning Decision Trees from Data

Exercises

Further Reading

6 BUILDING MODELS FROM DATA

6.1 Overview

6.2 Linear Regression

6.3 Logistic Regression

6.4

K

-Nearest Neighbors

6.5 Classification and Regression Trees

6.6 Other Approaches

Exercises

Further Reading

APPENDIX A ANSWERS TO EXERCISES

APPENDIX B HANDS-ON TUTORIALS

B.1 Tutorial Overview

B.2 Access and Installation

B.3 Software Overview

B.4 Reading in Data

B.5 Preparation Tools

B.6 Tables and Graph Tools

B.7 Statistics Tools

B.8 Grouping Tools

B.9 Models Tools

B.10 Apply Model

B.11 Exercises

BIBLIOGRAPHY

INDEX

END USER LICENSE AGREEMENT

List of Tables

Chapter 2

TABLE 2.1

TABLE 2.2

TABLE 2.3

TABLE 2.4

TABLE 2.5

TABLE 2.6

Chapter 3

TABLE 3.1

TABLE 3.2

TABLE 3.3

Chapter 4

TABLE 4.1

TABLE 4.2

TABLE 4.3

TABLE 4.4

TABLE 4.5

TABLE 4.6

TABLE 4.7

TABLE 4.8

Chapter 5

TABLE 5.1

TABLE 5.2

TABLE 5.3

TABLE 5.4

TABLE 5.5

TABLE 5.6

TABLE 5.7

TABLE 5.8

TABLE 5.9

TABLE 5.10

TABLE 5.11

TABLE 5.12

TABLE 5.13

TABLE 5.14

TABLE 5.15

TABLE 5.16

TABLE 5.17

TABLE 5.18

Chapter 6

TABLE 6.1

TABLE 6.2

TABLE 6.3

TABLE 6.4

TABLE 6.5

TABLE 6.6

TABLE 6.7

TABLE 6.8

TABLE 6.9

TABLE 6.10

TABLE 6.11

TABLE 6.12

TABLE 6.13

TABLE 6.14

TABLE 6.15

TABLE 6.16

TABLE 6.17

Appendix A

TABLE A.1

TABLE A.2

TABLE A.3

TABLE A.4

TABLE A.5

TABLE A.6

TABLE A.7

TABLE A.8

Appendix B

TABLE B.1

TABLE B.2

List of Illustrations

Chapter 1

FIGURE 1.1

Summary of a general framework for a data analysis project.

FIGURE 1.2

Summary of some of the issues to consider when defining and planning a data analysis project.

FIGURE 1.3

Summary of steps to consider when preparing the data.

FIGURE 1.4

Summary of tasks to consider when analyzing the data.

FIGURE 1.5

Summary of deployment options.

FIGURE 1.6

Summary of steps to consider in developing a data analysis or data mining project.

Chapter 2

FIGURE 2.1

Spreadsheet showing a sample of car observation.

FIGURE 2.2

Bar chart for the

Origin

variable from the auto-MPG data table.

FIGURE 2.3

Bar charts for the

Origin

variables from the auto-MPG data table showing the proportion and percentage.

FIGURE 2.4

Bar chart for a variable measured on an ordinal scale,

PLT

.

FIGURE 2.5

Frequency histogram for the variable “Acceleration.”

FIGURE 2.6

Examples of frequency distributions.

FIGURE 2.7

More complex frequency distributions.

FIGURE 2.8

Overview of elements of a box plot.

FIGURE 2.9

Box plot for the variable

MPG

.

FIGURE 2.10

Comparison of frequency histogram and a box plot for the variable

MPG

.

FIGURE 2.11

A box plot with extreme values explicitly shown as circles.

FIGURE 2.12

Frequency distribution showing a positive skew.

FIGURE 2.13

Skewness estimates for two variables.

FIGURE 2.14

Kurtosis estimates for two variables.

FIGURE 2.15

Illustration of the standard

z

-distribution to calculate

z

α/2

.

FIGURE 2.16

Standard

z

-distribution.

Chapter 3

FIGURE 3.1

Histogram showing an outlier.

FIGURE 3.2

Log transformation converting a variable (IC50) to adjust the frequency distribution.

Chapter 4

FIGURE 4.1

Example of a scatterplot where each point corresponds to an observation.