| Mathematics | |
This chapter introduces MATLAB's data analysis capabilities. It discusses how to organize arrays for data analysis, how to use simple descriptive statistics functions, and how to perform data preprocessing tasks in MATLAB. It also discusses other data analysis topics, including regression, curve fitting, data filtering, and fast Fourier transforms (FFTs). It includes:
Organizing arrays for data analysis.
Basic data analysis functions and an example that uses some of the functions. This section also discusses functions for the computation of correlation coefficients and covariance, and for finite difference calculations.
Working with missing values, and outliers or misplaced data points in a data set.
Investigates the use of different regression methods to find functions that describe the relationship among observed variables.
Uses a case study to look at some of MATLAB's basic data analysis capabilities. This section also provides information about the Basic Fitting interface.
Difference Equations and Filtering
Discusses MATLAB functions for working with difference equations and filters.
Fourier Analysis and the Fast Fourier Transform (FFT)
Discusses Fourier analysis in MATLAB
Data Analysis and Statistics Functions
The data analysis and statistics functions are in the directory datafun in the MATLAB Toolbox. Use online help to get a complete list of functions.
Related Toolboxes
A number of related toolboxes provide advanced functionality for specialized data analysis applications.
| Toolbox |
Data Analysis Application |
| Optimization |
Nonlinear curve fitting and regression. |
| Signal Processing |
Signal processing, filtering, and frequency analysis. |
| Spline |
Curve fitting and regression. |
| Statistics |
Advanced statistical analysis, nonlinear curve fitting, and regression. |
| System Identification |
Parametric / ARMA modeling. |
| Wavelet |
Wavelet analysis. |
| | Selected Bibliography | Column-Oriented Data Sets | |