Training - Courses

MLST: Statistical Methods in MATLAB

This two-day course provides hands-on experience with performing statistical data analysis with MATLAB® and
Statistics Toolbox™. Examples and exercises demonstrate the use of appropriate MATLAB and Statistics Toolbox
functionality throughout the analysis process, from importing and organizing data to exploratory analysis to
confirmatory analysis and simulation. Topics include:

Managing data
Calculating summary statistics
Visualizing data
Fitting distributions
Performing tests of significance
Performing analysis of variance
Fitting regression models
Reducing data sets
Generating random numbers and performing simulations

Note: A 1 hour test session will be scheduled one day prior to the first day of class. This session is to verify that the visual and audio connection is working properly on your computer. The required product software should be installed for the test session. It is highly recommended that you attend this session to ensure a successful and timely class start.

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	Detailed course outline

Day 1
Importing and Organizing Data	Objective: Understand the import methods and data types available in MATLAB and Statistics Toolbox to bring data into MATLAB and organize it for analysis. Common tasks, such as merging data and dealing with missing data, are highlighted. Importing data Data types Dataset arrays Merging data Categorical data Missing data
Exploring Data	Objective: Understand basic statistical investigation of a data set, including visualization and calculation of summary statistics. Plotting Central tendency Spread Shape Correlations Grouped data
Distributions	Objective: Use the functionality in Statistics Toolbox to investigate different probability distributions and to fit distributions to a data set. Probability distributions Distribution parameters Comparing and fitting distributions Nonparametric fitting Distribution objects
Hypothesis Tests	Objective: Use Statistics Toolbox to determine how likely an assertion about a data set is. Common uses of hypothesis tests, such as comparing two distributions and determining confidence intervals for a sample mean, are highlighted. Hypothesis tests Tests for normal distributions Tests for nonnormal distributions

Day 2
Analysis of Variance	Objective: Use Statistics Toolbox functions to compare the sample means of multiple groups and to find statistically significant differences between groups. Multiple comparisons One-way ANOVA N-way ANOVA MANOVA Nonnormal ANOVA Categorical correlations
Regression	Objective: Perform predictive modeling by fitting linear and nonlinear models to a data set. Techniques for improving model quality are highlighted. Linear regression models The design matrix Performing linear regressions Adding and paring predictors Increasing robustness Generalized linear regression Nonlinear regression
Working with Multiple Dimensions	Objective: Become familiar with techniques for reducing the dimensionality of a data set, and perform classification of categorical responses. Feature transformation Feature selection Classification Clustering
Random Numbers and Simulation	Objective: Use random numbers to evaluate the uncertainty or sensitivity of a model, or perform simulations. Generating random numbers from various distributions and managing the MATLAB random number generation algorithms are highlighted. Bootstrapping and simulation Random number generators Random streams The default stream Arbitrary distributions

Prerequisites

MATLAB Fundamentals

Course Length - 2 days