Data visualization toolbox

# 1 Introduction

- Software package
- Documentation table of contents
- Index of major functions

Data visualization uses graphical and numerical tools to reveal the information contained in data. It is a more effective approach for understanding and communication than the use of common numerical tools alone. This software package focuses on easily producible and interpretable displays which provide maximum insight. Most of this material is a blatant ripoff of William S. Cleveland's groundbreaking book, Visualizing Data. This book is must reading for any serious worker in the data analysis field.

Check price and availability of Visualizing Data
In the United States | Elsewhere

Key points of this approach are:

- Visualization is needed. Simple computation of standard parameters from a data set can lead to conclusions which are wrong, even very wrong. Some day even regulatory agencies may realize this.
- Robust statistical techniques are often required. The normal distribution only sometimes describes real data. The techniques taught in Statistics 101 are elegant, but far from the whole story.
- Graphical methods matter. The best visualization method is the one which supports the most insight into the phenomenon under study.

## The software package

The intent of this software package is to make the approach of Visualizing Data accessible to Matlab users. The package includes 26 mat files which contain all of the data files used in the book. It also includes function m files which implement most of the computation and display methods used in the book and script m files which produce all of the figures in this documentation.

In this documentation the new Matlab functions are in `green code` font. Many of the figure captions include a reference such as (book 3.15), which indicates that the figure corresponds to Figure 3.15 in Visualizing Data.

## Documentation table of contents

### Introduction | Documentation table of contents Index of major functions
### Univariate Data | Characterizing a distribution Comparing distributions Transforming data
### Bivariate Data | Curve fitting Visualizing residuals Robust fitting Discrete values Time series Seasonal components
### Trivariate Data | Scatterplots Coplots Image plots Surface fitting Contour plots Mesh plots Surface plots
### Hypervariate Data | Scatterplots Color scatterplots
### Multiway Data | Multway dot plots Additive fits Color scatterplots

## Index of major functions

- `addfit.m`
- `bank45.m`
- `bisquare.m`
- `coplot.m`
- `dotplot.m`
- `equalcount.m`
- `jitter.m`
- `loess.m`
- `mad.m`
- `mdplot.m`
- `multidotplot.m`
- `normalqqplot.m`
- `powerxform.m`
- `qqplot.m`
- `quantile.m`
- `quantileplot.m`
- `rdplot.m`
- `rfplot.m`
- `scattermatrix.m`
- `scattermatrixc.m`
- `seasonaloess.m`
- `slplot.m`
- `standardize.m`
- `trimmedmean.m`
- `trimmedstd.m`

Send feedback to author@datatool.com | To Data visualization home