STATISTICS IN MATLAB A PRIMER 1st Edition by MoonJung Cho, Wendy L. Martinez – Ebook PDF Instant Download/Delivery: 1040073972, 9781040073971
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STATISTICS IN MATLAB A PRIMER 1st Table of contents:
Chapter 1 MATLAB ® Basics
1.1 Desktop Environment
Command Window
Toolstrip Ribbon and Menus
Workspace
Script Editor
1.2 Getting Help and Other Documentation
Command Line Options
Documentation
User Community
1.3 Data Import and Export
1.3.1 Data I/O via the Command Line
Text Files
Comma-Separated Value (.csv) Files
Spreadsheets
Files with Characters and Numbers
1.3.2 The Import Wizard
1.3.3 Examples of Data I/O in MATLAB®
Loading and Saving .mat Files
Loading and Saving Text Files
Import Wizard
1.3.4 Data I/O with the Statistics Toolbox
Tabular Data:
SAS Files
1.3.5 More Functions for Data I/O
1.4 Data in Matlab®
1.4.1 Data Objects in Base MATLAB®
Arrays
Building Numeric Arrays
Empty Arrays
Cell Arrays
Structures
Tables
1.4.2 Accessing Data Elements
Array Elements
Accessing Structure Elements
Accessing Table Elements
1.4.3 Examples of Joining Data Sets
Stacking Arrays
Joining Along a Row
1.4.4 Data Types in the Statistics Toolbox
Dataset Array
1.4.5 Object-Oriented Programming
1.5 Miscellaneous Topics
1.5.1 File and Workspace Management
1.5.2 Punctuation in MATLAB®
1.5.3 Arithmetic Operators
1.5.4 Functions in MATLAB®
Function syntax
Command Syntax
Examples of Syntax
1.6 Summary and Further Reading
Chapter 2 Visualizing Data
2.1 Basic Plot Functions
2.1.1 Plotting 2-D Data
2.1.2 Plotting 3-D Data
2.1.3 Examples
2.2 Scatter Plots
2.2.1 Basic 2-D and 3-D Scatter Plots
2.2.2 Scatter Plot Matrix
2.2.3 Examples
2.3 Guis for Graphics
2.3.1 Simple Plot Editing
2.3.2 Plotting Tools Interface
2.3.3 plots Tab
2.4 Summary and Further Reading
Chapter 3 Descriptive Statistics
3.1 Measures of Location
3.1.1 Means, Medians, and Modes
3.1.2 Examples
3.2 Measures of Dispersion
3.2.1 Range
3.2.2 Variance and Standard Deviation
3.2.3 Covariance and Correlation
3.2.4 Examples
3.3 Describing the Distribution
3.3.1 Quantiles
3.3.2 Interquartile Range
3.3.3 Skewness
3.3.4 Examples
3.4 Visualizing the Data Distribution
3.4.1 Histograms
3.4.2 Probability Plots
3.4.3 Boxplots
3.4.4 Examples
3.5 Summary and Further Reading
Chapter 4 Probability Distributions
4.1 Distributions in Matlab®
4.1.1 Continuous Distributions
4.1.2 Discrete Distributions
4.1.3 Probability Distribution Objects
4.1.4 Other Distributions
Example of Kernel Density Estimation
4.1.5 Examples of Probability Distributions in MATLAB®
4.1.6 disttool for Exploring Probability Distributions
4.2 Parameter Estimation
4.2.1 Command Line Functions
4.2.2 Examples of Parameter Estimation
4.2.3 dfittool for Interactive Fitting
4.3 Generating Random Numbers
4.3.1 Generating Random Variables in Base MATLAB®
4.3.2 Generating Random Variables with the Statistics Toolbox
4.3.3 Examples of Random Number Generation
4.3.4 randtool for Generating Random Variables
4.4 Summary and Further Reading
Chapter 5 Hypothesis Testing
5.1 Basic Concepts
5.1.1 Hypothesis Testing
5.1.2 Confidence Intervals
5.2 Common Hypothesis Tests
5.2.1 The z-Test and t-Test
5.2.2 Examples of Hypothesis Tests
5.3 Confidence Intervals Using Bootstrap Resampling
5.3.1 The Basic Bootstrap
5.3.2 Examples
5.4 Analysis of Variance
5.4.1 One-Way ANOVA
5.4.2 ANOVA Example
5.5 Summary and Further Reading
Chapter 6 Model-Buil ding with Regression Analysis
6.1 Introduction to Linear Models
6.1.1 Specifying Models
6.1.2 The Least Squares Approach for Estimation
6.1.3 Assessing Model Estimates
6.2 Model-Building Functions in Base Matlab®
6.2.1 Fitting Polynomials
6.2.2 Using the Division Operators
6.2.3 Ordinary Least Squares
6.3 Functions in the Statistics Toolbox
6.3.1 Using regress for Regression Analysis
6.3.2 Using regstats for Regression Analysis
6.3.3 The Linear Regression Model Class
6.3.4 Assessing Model Fit
6.4 Basic Fitting Gui
6.5 Summary and Further Reading
Chapter 7 Multivariate Analysis
7.1 Principal Component Analysis
7.1.1 Functions for PCA in Base MATLAB®
7.1.2 Functions for PCA in the Statistics Toolbox
7.1.3 Biplots
7.2 Multidimensional Scaling—Mds
7.2.1 Measuring Distance
7.2.2 Classical MDS
7.2.3 Metric MDS
7.2.4 Nonmetric MDS
7.3 Visualization in Higher Dimensions
7.3.1 Scatter Plot Matrix
7.3.2 Parallel Coordinate Plots
7.3.3 Andrews Curves
7.4 Summary and Further Reading
Chapter 8 Classifica tion and Clustering
8.1 Supervised Learning or Classification
8.1.1 Bayes Decision Theory
8.1.2 Discriminant Analysis
8.1.3 Naive Bayes Classifiers
8.1.4 Nearest Neighbor Classifier
8.2 Unsupervised Learning or Cluster Analysis
8.2.1 Hierarchical Clustering
8.2.2 k-Means Clustering
8.3 Summary and Further Reading
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