Accelerating the pace of engineering and science

Solving Data Management and Analysis Challenges Using MATLAB and Statistics Toolbox

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Jiro Doke, MathWorks
Richard Willey, MathWorks

Engineers and scientists often need to invest significant amounts of time and effort analyzing large data sets. This task becomes even more complicated if sensor failures or drop outs result in bad or missing data points. Data management techniques can help mitigate these types of problems.

An example application will demonstrate how MATLAB and statistics add-on products can be used to organize information, compensate for missing data, and enhance data analysis.

This presentation will show you how to:
·Use dataset arrays to organize and analyze heterogeneous data/metadata
·Use categorical arrays to work with data that take on values from a finite set of levels (or categories)
·Use techniques such as filtering, mean/median replacement, interpolation, and regression substitution to remove missing data
·Perform Exploratory Data Analysis using interaction visualization tools
·Capture and model trends observed in the data

Previous knowledge of MATLAB is not required to attend this webinar.

Product Focus

  • Statistics and Machine Learning Toolbox
  • Curve Fitting Toolbox

Recorded: 14 Oct 2008