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Engineering_statistics_handbook__2006

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1. Exploratory Data Analysis

1. Exploratory Data Analysis
This chapter presents the assumptions, principles, and techniques necessary to gain
insight into data via EDA--exploratory data analysis.
1. EDA Introduction

2. EDA Assumptions

1. What is EDA?

1. Underlying Assumptions

2. EDA vs Classical & Bayesian

2. Importance

3. EDA vs Summary
4. EDA Goals

3. Techniques for Testing
Assumptions

5. The Role of Graphics

4. Interpretation of 4-Plot

6. An EDA/Graphics Example

5. Consequences

7. General Problem Categories
3. EDA Techniques

4. EDA Case Studies

1. Introduction

1. Introduction

2. Analysis Questions

2. By Problem Category

3. Graphical Techniques: Alphabetical
4. Graphical Techniques: By Problem
Category
5. Quantitative Techniques
6. Probability Distributions
Detailed Chapter Table of Contents
References
Dataplot Commands for EDA Techniques

 [5/1/2006 9:56:13 AM]

1. Exploratory Data Analysis

1. Exploratory Data Analysis - Detailed Table of
Contents [1.]
This chapter presents the assumptions, principles, and techniques necessary to gain insight into
data via EDA--exploratory data analysis.
1. EDA Introduction [1.1.]
1. What is EDA? [1.1.1.]
2. How Does Exploratory Data Analysis differ from Classical Data Analysis? [1.1.2.]
1. Model [1.1.2.1.]
2. Focus [1.1.2.2.]
3. Techniques [1.1.2.3.]
4. Rigor [1.1.2.4.]
5. Data Treatment [1.1.2.5.]
6. Assumptions [1.1.2.6.]
3. How Does Exploratory Data Analysis Differ from Summary Analysis? [1.1.3.]
4. What are the EDA Goals? [1.1.4.]
5. The Role of Graphics [1.1.5.]
6. An EDA/Graphics Example [1.1.6.]
7. General Problem Categories [1.1.7.]
2. EDA Assumptions [1.2.]
1. Underlying Assumptions [1.2.1.]
2. Importance [1.2.2.]
3. Techniques for Testing Assumptions [1.2.3.]
4. Interpretation of 4-Plot [1.2.4.]
5. Consequences [1.2.5.]
1. Consequences of Non-Randomness [1.2.5.1.]
2. Consequences of Non-Fixed Location Parameter [1.2.5.2.]

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1. Exploratory Data Analysis

3. Consequences of Non-Fixed Variation Parameter [1.2.5.3.]
4. Consequences Related to Distributional Assumptions [1.2.5.4.]
3. EDA Techniques [1.3.]
1. Introduction [1.3.1.]
2. Analysis Questions [1.3.2.]
3. Graphical Techniques: Alphabetic [1.3.3.]
1. Autocorrelation Plot [1.3.3.1.]
1. Autocorrelation Plot: Random Data [1.3.3.1.1.]
2. Autocorrelation Plot: Moderate Autocorrelation [1.3.3.1.2.]
3. Autoco...
1.Exploratory Data Analysis
This chapter presents the assumptions, principles, and techniques necessary to gain
insight into data via EDA--exploratory data analysis.
1. EDA Introduction
What is EDA?1.
EDA vs Classical & Bayesian2.
EDA vs Summary3.
EDA Goals4.
The Role of Graphics5.
An EDA/Graphics Example6.
General Problem Categories7.
2. EDA Assumptions
Underlying Assumptions1.
Importance2.
Techniques for Testing
Assumptions
3.
Interpretation of 4-Plot4.
Consequences5.
3. EDA Techniques
Introduction1.
Analysis Questions2.
Graphical Techniques: Alphabetical3.
Graphical Techniques: By Problem
Category
4.
Quantitative Techniques5.
Probability Distributions6.
4. EDA Case Studies
Introduction1.
By Problem Category2.
Detailed Chapter Table of Contents
References
Dataplot Commands for EDA Techniques
1. Exploratory Data Analysis
http://www.itl.nist.gov/div898/handbook/eda/eda.htm [5/1/2006 9:56:13 AM]
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