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Training - Courses

MLST: Statistical Methods in MATLAB

This course provides an introduction to statistical tools in MATLAB, Statistics Toolbox, and the Curve Fitting Toolbox including:

  • Data file input and output
  • Handling large and incommensurate data sets
  • Computing descriptive statistics
  • Statistical plotting and visualization
  • Fitting distributions to data
  • Bivariate regression
  • Random number generators, simulations, and hypothesis tests
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 Detailed course outline

 

Day 1 of 1
Introduction

Objectives:

  • Obtain a quick overview of The MathWorks and the family of products
  • Discuss course set-up, materials, and logistics
  • Provide a “big picture” view of the course ahead
Data and Statistics

Objective: Learn to work with data in the MATLAB environment, compute basic descriptive statistics, and visualize data in a variety of ways

  • What is statistics?
  • Statistical sampling and modeling
  • Statistical questions
  • Data analysis
Working with data
  • Incommensurate data
  • Missing data
Descriptive statistics
  • Measures of center, spread, and shape
Statistical plotting
  • Histograms, scatter plots, and box plots
  • Grouped data
Distributions and Bootstrapping

Objective: Explore the variety of probability distributions available in the Statistics Toolbox

Distribution concepts
  • Discrete distributions
  • Continuous distributions
  • Distributions in the Statistics Toolbox
  • Distribution parameters
  • Computing probabilities
Data and distributions
  • Sampling distributions
  • Choosing a distribution
  • Parameter estimation
  • Nonparametric density functions
  • Bootstrapping and simulation
  • Distribution testing
Exercise
  • Distribution diagnostics
Regression Analysis

Objective: Explore regression analysis for bivariate data

  • Regression concepts
  • Predictors and responses
  • Linear and nonlinear models
  • Scatter plots
  • Correlation and covariance

 

Linear methods
  • Quantiles and quantile plots
  • Solving systems of linear equations with the backslash operator
  • Linear least squares
  • Polynomial fitting
  • Graphical user interface tools for linear regression
  • Curve Fitting Toolbox
  • Generalized linear models
Nonlinear methods
  • Graphical user interface tools for nonlinear regression
  • Using the Curve Fitting Toolbox for nonlinear regression
Exercise
  • National debt
Hypothesis Tests

Objective:  Explore the various hypothesis test within the toolbox

  • Terminology
  • Assumptions
  • Tests in the Statistics Toolbox

Prerequisites

Working knowledge of the MATLAB language and basic statistics.

Class Times: September 28-30, 2010: 8:30a - 12:30p (GMT-4:00)


Please Note: A 1 hour test session will be scheduled on the first day of class. This test session will cover viewing and audio troubleshooting as well as software installation (content will be presented during the remaining days). It is highly recommended that you attend this session to ensure a successful start to the class.

Course Length - 2 half-days

Price - $650.00

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