Fulfilling the need for a practical user’s guide, Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB® and its extensive functionality for statistics. Assuming a basic knowledge of statistics and probability as well as a fundamental understanding of linear algebra concepts, this book:
- Covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB
- Presents examples of how MATLAB can be used to analyze data
- Offers access to a companion website with data sets and additional examples
- Contains figures and visual aids to assist in application of the software
- Explains how to determine what method should be used for analysis
Statistics in MATLAB: A Primer is an ideal reference for undergraduate and graduate students in engineering, mathematics, statistics, economics, biostatistics, and computer science. It is also appropriate for a diverse professional market, making it a valuable addition to the libraries of researchers in statistics, computer science, data mining, machine learning, image analysis, signal processing, and engineering.
List of Tables
Preface
MATLAB Basics
Desktop Environment
Getting Help and Other Documentation
Data Import and Export
Data I/O via the Command Line
The Import Wizard
Examples of Data I/O in MATLAB
Data I/O with the Statistics Toolbox
More Functions for Data I/O
Data in MATLAB
Data Objects in Base MATLAB
Accessing Data Elements
Examples of Joining Data Sets
Data Types in the Statistics Toolbox
Object–Oriented Programming
Miscellaneous Topics
File and Workspace Management
Punctuation in MATLAB
Arithmetic Operators
Functions in MATLAB
Summary and Further Reading
Visualizing Data
Basic Plot Functions
Plotting 2–D Data
Plotting 3–D Data
Examples
Scatter Plots
Basic 2–D and 3–D Scatter Plots
Scatter Plot Matrix
Examples
GUIs for Graphics
Simple Plot Editing
Plotting Tools Interface
PLOTS Tab
Summary and Further Reading
Descriptive Statistics
Measures of Location
Means, Medians, and Modes
Examples
Measures of Dispersion
Range
Variance and Standard Deviation
Covariance and Correlation
Examples
Describing the Distribution
Quantiles
Interquartile Range
Skewness
Examples
Visualizing the Data Distribution
Histograms
Probability Plots
Boxplots
Examples
Summary and Further Reading
Probability Distributions
Distributions in MATLAB
Continuous Distributions
Discrete Distributions
Probability Distribution Objects
Other Distributions
Examples of Probability Distributions in MATLAB
disttool for Exploring Probability Distributions
Parameter Estimation
Command Line Functions
Examples of Parameter Estimation
difittool for Interactive Fitting
Generating Random Numbers
Generating Random Variables in Base MATLAB
Generating Random Variables in the Statistics Toolbox
Examples of Random Number Generation
randtool for Generating Random Variables
Summary and Further Reading
Hypothesis Testing
Basic Concepts
Hypothesis Testing
Confidence Intervals
Common Hypothesis Tests
The z–test and t–test
Examples of Hypothesis Tests
Confidence Intervals using Bootstrap Resampling
The Basic Bootstrap
Examples
Analysis of Variance
One–Way ANOVA
ANOVA Example
Summary and Further Reading
Model–Building with Regression Analysis
Introduction to Linear Models
Specifying Models
The Least Squares Approach for Estimation
Assessing Model Estimates
Model–Building Functions in Base MATLAB
Fitting Polynomials
Using the Division Operators
Ordinary Least Squares
Functions in the Statistics Toolbox
Using regress for Regression Analysis
Using regstats for Regression Analysis
The Linear Regression Model Class
Assessing Model Fit
Basic Fitting GUI
Summary and Further Reading
Multivariate Analysis
Principal Component Analysis
Functions for PCA in Base MATLAB
Functions for PCA in the Statistics Toolbox
Biplots
Multidimensional Scaling—MDS
Measuring Distance
Classical MDS
Metric MDS
Nonmetric MDS
Visualization in Higher Dimensions
Scatter Plot Matrix
Parallel Coordinate Plots
Andrews Curves
Summary and Further Reading
Classification and Clustering
Supervised Learning or Classification
Bayes Decision Theory
Discriminant Analysis
Naive Bayes Classifiers
Nearest Neighbor Classifier
Unsupervised Learning or Cluster Analysis
Hierarchical Clustering
K–Means Clustering
Summary and Further Reading
References
Index of MATLAB Functions
Subject Index
Biography
Wendy L. Martinez is a mathematical statistician with the Bureau of Labor Statistics in Washington, District of Columbia, USA. She has co-authored two additional successful Chapman Hall/CRC books on MATLAB and statistics, and has been using MATLAB for more than 15 years to solve problems and conduct research in statistics and engineering.
MoonJung Cho is a mathematical statistician with the Bureau of Labor Statistics in Washington, District of Columbia, USA. She has more than10 years of experience in survey methodology research and applications, and is knowledgeable of other software packages, such as SAS and R. She is able to use this knowledge to enhance the utility of this book to users of other statistical software packages.
"The book provides an introductory but comprehensive guide for performing data analysis in MATLAB. It not only covers the most important topics in basic statistics (along with some machine learning techniques), but also touches upon more advanced methods such as kernel density estimation, bootstrap, and principal component analysis…Most of the theories are conveyed in a concise and intuitive way, yet the explanations are quite effective. The implementation of each method in MATLAB is demonstrated using real examples. Detailed MATLAB codes and corresponding numerical and figure outputs are presented with informative MATLAB comments, which makes them easily understood even without the context. The book can be used as a good complementary book to introductory statistics courses…The book can also serve as a perfect guide for self-learners who are not familiar with MATLAB but wish to use MATLAB as a data analysis tool."
—The American Statistician