## Statistical Methods in MATLAB

Learn how to use MATLAB to perform statistical analysis with distribution fitting, regression, and hypothesis testing

### 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 significance
• Performing analysis of variance
• Fitting regression models
• Reducing data sets
• Generating random numbers and performing simulations

### 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

TechSource Systems is MathWorks Authorised Reseller and Training Partner

#### Upcoming Program

• Please keep me posted on the next schedule

## Course Outline

#### 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
• Customizing plots

#### Exploring Data

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

• Plotting
• Central tendency
• Shape
• Correlations
• Grouped data
• Getting help
• Creating and running scripts

#### Distributions

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

• 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.

• Hypothesis tests
• Tests for normal distributions
• Tests for nonnormal distributions

#### Analysis of Variance

Objective: 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

Objective: 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
• Logistic and generalized linear regression
• Nonlinear regression

#### Working with Multiple Dimensions

Objective: Simplify high-dimensional data sets by reducing the dimensionality.

• 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.

• Bootstrapping and simulation
• Generating numbers from standard distributions
• Generating numbers from arbitrary distributions
• Controlling the random number stream
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