STATISTICAL METHODS with MATLAB

The aim of this book is to introduce the reader to the statistical methods. Statistics Toolbox provides algorithms and tools for organizing, analyzing, and modeling data. You can use regression or classification for predictive modeling, 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.

The essential content of the book is as follows:

Descriptive Statistics
Exploratory Analysis of Data
Resampling Statistics
Bootstrap
Jackknife
Parallel Computing Support for Resampling Methods
Data with Missing Values
Statistical Visualization
Scatter Plots
Box Plots
Distribution Plots
Probability Distributions
Statistics Toolbox Distribution Functions
Gaussian Mixture Models
Hypothesis Tests
Analysis of Variance
One-Way ANOVA
Two-Way ANOVA
N-Way ANOVA
Analysis of Covariance
MANOVA
Multivariate Methods
Multivariate Linear Regression
Multivariate General Linear Model
Fixed Effects Panel Model with Concurrent
Multidimensional Scaling
Feature Transformation
Principal Component Analysis (PCA)
Factor Analysis
Partial Least Squares Regression and Principal
Components Regression
Cluster Analysis
Hierarchical Clustering
k-Means Clustering
Gaussian Mixture Models
Introduction to Gaussian Mixture Models
Cluster with Gaussian Mixtures
Parametric Classification
Discriminant Analysis
Naive Bayes Classification
Design of Experiments
Full Factorial Designs
Multilevel Designs
Two-Level Designs
Fractional Factorial Designs
Plackett-Burman Designs
General Fractional Designs
Response Surface Designs
Box-Behnken Designs
D-Optimal Designs
Statistical Process Control
Control Charts
Capability Studies