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Statistics in Matlab


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Table of Contents

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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

About the Author

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

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