Fulfilling the need for a practical user's guide, Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB(R) 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
