MATLAB & Simulink

DATA ANALYSIS AND MACHINE LEARNING

Statistical Methods in MATLAB

Course Highlights

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 signifiance

  • Performing analysis of variance

  • Fitting regression models

  • Reducing data sets

  • Generating random numbers and performing simulations

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Who Should Attend

Engineer, researchers, data scientists, and managers, who are involved in using statistical methods to analyse bigger, more complex data and deliver faster and more accurate results.

 
Course Prerequisites

MATLAB Fundamentals 


Course Benefits

Upon the completion of the course, the participants will be able to

- calculate summary statistics

- fit model data

- perform hypothesis tests

- use analysis of variance to test for difference in data groups

- reduce dimensionality of large data sets - generate random numbers for simulations 
 

Partners 

Upcoming Program

Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner

Course Outline

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.

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  • 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.

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  • Plotting

  • Central tendency

  • Spread

  • Shape

  • Correlations

  • Grouped data 

 

Distributions 

 

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

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  • 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.

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  • 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.

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  • 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. Explore techniques for improving model quality. 

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  • 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-dimentional data sets by reducing the dimensionality.

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  • 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.

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  • Bootstrapping and simulation

  • Generating numbers from standard distributions

  • Generating numbers from arbitrary distributions

  • Controlling the random number stream