MATLAB and Simulink Training

Statistical Methods in MATLAB

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Course Details

This 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, including importing and organizing data, exploratory analysis, 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 descriptive statistics on a data set, including visualization and calculation of summary statistics.

  • Visualizing data
  • Calculating parameters of location, spread, and shape
  • Computing correlation coefficients
  • Perform calculations with grouped data

Distributions

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

  • Probability distributions and their parameters
  • Comparing and fitting distributions
  • Fitting nonparametric distributions
  • Bootstrapping and simulation
  • Generating random numbers from arbitrary distributions
  • Controlling the random number stream

Day 2 of 2


Hypothesis Tests

Objective: Determine if a data set sufficiently supports a particular hypothesis. Apply hypothesis tests for common uses, such as comparing the location and spread parameters of two distributions.

  • Confirmatory data analysis
  • Hypothesis tests for normal distributions
  • Hypothesis tests for nonnormal distributions

Analysis of Variance

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

  • Performing Analysis of Variance (ANOVA)
  • Computing corrections for multiple comparisons
  • Performing N-way ANOVA and Multivariate Analysis of Variance (MANOVA)
  • ANOVA for non-normal data
  • Independence tests for categorical data

Regression

Objective: Perform predictive modeling by fitting linear and nonlinear models to a data set. Explore techniques for improving model quality. Simplify high-dimensional data sets by reducing the dimensionality.

  • Linear regression models
  • Fitting linear models to data
  • Evaluating the fit and adjusting the model
  • Logistic and generalized linear regression
  • Nonlinear regression
  • Feature selection and transformation

Level: Intermediate

Prerequisites:

Duration: 2 days

Languages: English, 日本語, 한국어

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