Statistical Methods in MATLAB

Who Should Attend?

Data Scientist, engineers and managers who need to analyse signals (time series data) for data analytics and predictive maintenance applications

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Duration: 2 Days

Having the skills and tools that can do data analysis efficiently are important to help company to make sense of the data.

This two-day course provides hands-on experience with performing statistical data analysis with MATLAB and Statistics and Machine Learning Toolbox. Examples and exercises demonstate 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
  • Import and organize data
  • Preprocess time-based signals and extract key features in the time and frequency domains
  • Build classification and regression model using Statistic and Machine Learning toolbox
  • Ensemble data and train model to predict remaining useful life (RUL) with Predictive Maintenance Toolbox

DAY 1 of 2

Importing and Organizing Data

Objectives: Bring data into MATLAB and organize it for analysis. Perform common tasks, such as merging data and dealing with missing data. Import, visualize, and browse signals to gain insights

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

Exploring Data

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

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

Distributions

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

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

Hypothesis Tests

Objectives: 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 non-normal distributions

Day 2 of 2

Analysis of Variance

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

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

Regression

Objectives: 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

Objectives: Simplify high-dimentional data sets by reducing the dimensionality.

  • Feature transformation
  • Feature selection

Random Numbers and Simulation

Objectives: 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

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