Exploratory Analysis Of Data And Descriptive Statistics With Matlab

Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data. You can use descriptive statistics and plots for exploratory data analysis, fit probability distributions to data, generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models. For multidimensional data analysis, Statistics and Machine Learning Toolbox provides feature selection, stepwise regression, principal component analysis (PCA), regularization, and other dimensionality reduction methods that let you identify variables or features that impact your model. The toolbox provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Many of the statistics and machine learning algorithms can be used for computations on data sets that are too big to be stored in memory.. This book develops organizing data techniques, descriptive statistics, plots for exploratory data analysis, data visualization techniques and other exploratory techniques across examples using MATLAB.

The aim of this book is to introduce the reader to the techniques of descriptive statistics and exploratory data analysis..Statistics Toolbox provides algorithms and tools for organizing, analyzing, and modeling data. You can use regression or classification for predictive modeling, generate random numbers for Monte Carlo simulations, use statistical plots for exploratory data analysis, and perform hypothesis tests. For analyzing multidimensional data, Statistics Toolbox includes algorithms that let you identify key variables that impact your model with sequential feature selection, transform your data with principal component analysis, apply regularization and shrinkage, or use partial least-squares regression. Statistics Toolbox includes specialized data types for organizing and accessing heterogeneous data. Dataset arrays store numeric data, text, and metadata in a single data container. Built-in methods enable you to merge datasets using a common key (join), calculate summary statistics on grouped data, and convert between tall and wide data representations. Categorical arrays provide a memory-efficient data container for storing information drawn from a finite, discrete set of categories.

Praise for the Second Edition: "The authors present an intuitive and easy-to-read book. ... accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB." —Adolfo Alvarez Pinto, International Statistical Review "Practitioners of EDA who use MATLAB will want a copy of this book. ... The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. —David A Huckaby, MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book’s website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions, such as beanplots and violin plots A chapter on visualizing categorical data

Exploratory data analysis (EDA) was conceived at a time when computers were not widely used, and thus computational ability was rather limited. As computational sophistication has increased, EDA has become an even more powerful process for visualizing and summarizing data before making model assumptions to generate hypotheses, encompassing larger a

This book develops the first part of the work that is carried out in data science. It is essential to start any investigation with exploratory data analysis. Within the exploratory analysis, data structures, matrix language, descriptive statistics and the graphic representation of information play an important role. These tasks are the essential content of this book

This primer provides an accessible introduction to MATLAB version 8 and its extensive functionality for statistics. Fulfilling the need for a practical user's guide, the book covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB, presenting examples of how MATLAB can be used to analyze data. It explains how to determine what method should be used for analysis, and includes figures, visual aids, and access to a companion website with data sets and additional examples.

The contents of this book is focused on basic data analysis with MATLAB. Initially the import and export of data, key tasks in any kind of analysis is studied. Then numerical and graphical data exploratory analysis is presented. The next block of content is focused on descriptive statistics and correlation analysis, covariance and regression. These topics are expanded to the study of model simple and multiple linear regression and Curve Fitting. Polynomial regression and nonlinear regression is also studied. Finally an important piece of content is devoted to the time series analysis in interactive mode and command mode. The following topics are developed: Importing and Exporting Data Plotting Data Missing Data Inconsistent Data Filtering Data Filter Function Moving Average Filter Discrete Filter Detrending Data Removing Linear Trends from Data Differencing Data Descriptive Statistics Functions for Calculating Descriptive Statistics Interactive Data Exploration Interacting with MATLAB Data Graphs Marking Up Graphs with Data Brushing Effects of Brushing on Data Making Graphs Responsive with Data Linking How to Link Plots How Linked Plots Behave Linking vs. Refreshing Plots Using Linked Plot Controls Interacting with Graphed Data Data Brushing with the Variables Editor Using Data Tips to Explore Graphs Regression Analysis Linear Correlation Covariance Correlation Coefficients Linear Regression Residuals and Goodness of Fit Fitting Data with Curve Fitting Toolbox Functions Interactive Fitting The Basic Fitting GUI Preparing for Basic Fitting Opening the Basic Fitting GUI Programmatic Fitting MATLAB Functions for Polynomial Models Linear Model with Nonpolynomial Terms Multiple Regression Time Series Analysis Time Series Objects Time Series Data Sample Time Series Objects and Methods Time Series Constructor Time Series Collection Constructor Time Series Tools Importing and Exporting Data Plotting Time Series Selecting Data for Analysis Editing Data, Time, Attributes, and Events Processing and Manipulating Time Series

The contents of this book is focused on basic data analysis with MATLAB. Initially the import and export of data, key tasks in any kind of analysis is studied. Then numerical and graphical data exploratory analysis is presented. The next block of content is focused on descriptive statistics and correlation analysis, covariance and regression. These topics are expanded to the study of model simple and multiple linear regression and Curve Fitting. Polynomial regression and nonlinear regression is also studied. Finally an important piece of content is devoted to the time series analysis in interactive mode and command mode. The following topics are developed: Importing and Exporting Data Plotting Data Missing Data Inconsistent Data Filtering Data Filter Function Moving Average Filter Discrete Filter Detrending Data Removing Linear Trends from Data Differencing Data Descriptive Statistics Functions for Calculating Descriptive Statistics Interactive Data Exploration Interacting with MATLAB Data Graphs Marking Up Graphs with Data Brushing Effects of Brushing on Data Making Graphs Responsive with Data Linking How to Link Plots How Linked Plots Behave Linking vs. Refreshing Plots Using Linked Plot Controls Interacting with Graphed Data Data Brushing with the Variables Editor Using Data Tips to Explore Graphs Regression Analysis Linear Correlation Covariance Correlation Coefficients Linear Regression Residuals and Goodness of Fit Fitting Data with Curve Fitting Toolbox Functions Interactive Fitting The Basic Fitting GUI Preparing for Basic Fitting Opening the Basic Fitting GUI Programmatic Fitting MATLAB Functions for Polynomial Models Linear Model with Nonpolynomial Terms Multiple Regression Time Series Analysis Time Series Objects Time Series Data Sample Time Series Objects and Methods Time Series Constructor Time Series Collection Constructor Time Series Tools Importing and Exporting Data Plotting Time Series Selecting Data for Analysis Editing Data, Time, Attributes, and Events Processing and Manipulating Time Series

Since the publication of the bestselling first edition, many advances have been made in exploratory data analysis (EDA). Covering innovative approaches for dimensionality reduction, clustering, and visualization, Exploratory Data Analysis with MATLAB®, Second Edition uses numerous examples and applications to show how the methods are used in practice. New to the Second Edition Discussions of nonnegative matrix factorization, linear discriminant analysis, curvilinear component analysis, independent component analysis, and smoothing splines An expanded set of methods for estimating the intrinsic dimensionality of a data set Several clustering methods, including probabilistic latent semantic analysis and spectral-based clustering Additional visualization methods, such as a rangefinder boxplot, scatterplots with marginal histograms, biplots, and a new method called Andrews’ images Instructions on a free MATLAB GUI toolbox for EDA Like its predecessor, this edition continues to focus on using EDA methods, rather than theoretical aspects. The MATLAB codes for the examples, EDA toolboxes, data sets, and color versions of all figures are available for download at http://pi-sigma.info

MATLAB is currently the language of technical computing most known and used in academia, industry and services. It is composed of a set of tools and a very large number of functions, graphics objects with associated properties and operators.The book begins by looking at the main tools, in particular the Desktop, the Command and History Window, the Editor and the Help Browser. The selected number of functions, graphics objects, related properties and operators, considered fundamental in MATLAB, is a unique and remarkable feature of this book. These basic elements are minutely treated both formally and through examples.The arrangement of every data type as an array is another prominent emphasis of the book. Numerical data used in advanced mathematics usually defined as vectors or matrices are only one example. Others include logical values, strings of characters, dates, images, etc.Standard programming structures, like the many patterns of user functions and of the flow controls, are highlighted.The basic elements of data visualization — the main graphics objects and their properties — are also carefully examined.