MATLAB and Simulink Training

Statistical Methods in MATLAB

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Course Details

This two-day course provides hands-on experience for performing statistical data analysis with MATLAB® and Statistics and Machine Learning Toolbox™. Examples and exercises demonstrate the use of appropriate MATLAB and Statistics and Machine Learning 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

This program has been approved by GARP and qualifies for 14 GARP CPD credit hours. If you are a Certified FRM or ERP, please record this activity in your credit tracker.

Day 1 of 2

Importing and Organizing Data

Objective: Bring data into MATLAB and organize it for analysis. Perform common tasks, such as merging data and dealing with missing data.

  • Importing data
  • Data types
  • Tables of data
  • Merging data
  • Categorical data
  • Missing data

Exploring Data

Objective: Perform basic statistical investigation of a data set, including visualization and calculation of summary statistics.

  • Plotting
  • Central tendency
  • Spread
  • Shape
  • Correlations
  • Grouped data


Objective: Investigate different probability distributions and fit distributions to a data set.

  • Probability distributions
  • Distribution parameters
  • Comparing and fitting distributions
  • Nonparametric fitting

Hypothesis Tests

Objective: Determine how likely an assertion about a data set is. Apply hypothesis tests for common uses, such as comparing two distributions and determining confidence intervals for a sample mean.

  • Hypothesis tests
  • Tests for normal distributions
  • Tests for nonnormal distributions

Day 2 of 2

Analysis of Variance

Objective: Compare the sample means of multiple groups and find statistically significant differences between groups.

  • Multiple comparisons
  • One-way ANOVA
  • N-way ANOVA
  • Nonnormal ANOVA
  • Categorical correlations


Objective: Perform predictive modeling by fitting linear and nonlinear models to a data set. Explore techniques for improving model quality.

  • Linear regression models
  • Fitting linear models to data
  • Evaluating the fit
  • Adjusting the model
  • Logistic and generalized linear regression
  • Nonlinear regression

Working with Multiple Dimensions

Objective: Simplify high-dimensional data sets by reducing the dimensionality.

  • Feature transformation
  • Feature selection

Random Numbers and Simulation

Objective: Use random numbers to evaluate the uncertainty or sensitivity of a model, or perform simulations. Generate random numbers from various distributions, and manage the MATLAB random number generation algorithms.

  • Bootstrapping and simulation
  • Generating numbers from standard distributions
  • Generating numbers from arbitrary distributions
  • Controlling the random number stream

Level: Intermediate


Duration: 2 days

Languages: English, Français

View schedule and enroll