Training - Courses

Objectives:

• Obtain a quick overview of The MathWorks and the family of products
• Discuss course set-up, materials, and logistics
• Provide a “big picture” view of the course ahead

Objective: Learn to work with data in the MATLAB environment, compute basic descriptive statistics, and visualize data in a variety of ways

• What is statistics?
• Statistical sampling and modeling
• Statistical questions
• Data analysis

• Data I/O
• Tabular data and case lists
• Incommensurate data
• Missing data

• Measures of center, spread, and shape

• Histograms, scatter plots, and box plots
• Grouped data
• Preprocessing and reexpression

• Time series

Objective: Review the basics of probability and random variables and explore the variety of probability distributions available in the Statistics Toolbox

• Probability measures
• Random variables
• Probability distributions

• Discrete distributions
• Continuous distributions
• Distributions in the Statistics Toolbox
• Distribution parameters
• Computing probabilities

• Sampling distributions
• Choosing a distribution
• Parameter estimation
• Nonparametric density functions
• Bootstrapping and simulation
• Distribution testing

• Distribution diagnostics

Objective: Explore regression analysis for bivariate data

• Regression concepts
• Predictors and responses
• Linear and nonlinear models
• Scatter plots
• Correlation and covariance

• Quantiles and quantile plots
• Solving systems of linear equations with the backslash operator
• Linear least squares
• Polynomial fitting
• Graphical user interface tools for linear regression
• Curve Fitting Toolbox
• Generalized linear models

• Graphical user interface tools for nonlinear regression
• Using the Curve Fitting Toolbox for nonlinear regression

• National debt

Objective: Extend the concepts of the previous section to data sets with many variables and introduce specialized techniques for multivariate analysis and visualization

• Multivariate plotting
• 3-D scatter plots
• Response surfaces

• Principal component analysis
• Concepts
• Set-up and analysis

• Concepts
• Set-up and analysis

• Concepts
• Set-up and analysis
• Hierarchical clustering and k-means clustering

• Winning the decathlon

Objective: Understand the random number generators in MATLAB and the Statistics Toolbox and their use in Monte Carlo methods

• Pseudorandom numbers
• Randomness
• Multiplicative congruential algorithms

• rand and its algorithm

• randn and its algorithm

• Inverse transform method
• Acceptance-rejection method
• Random number generators in the Statistics Toolbox

• Integration
• Simulation

• Writing a random number generator
• Monte Carlo integration

Objective: Explore hypothesis testing and its application to analysis of variance

• Hypothesis tests
• Terminology
• Assumptions
• Tests in the Statistics Toolbox

• Set-up and analysis

• Set-up and analysis

• Set-up and analysis

• Set-up and analysis

• Nutritional data