# Statistical Methods in MATLAB

## Course Details

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

This program has been approved by GARP and qualifies for 14 GARP CPD credit hours. If you are a Certified FRM or ERP, please record this activity in your credit tracker at https://www.garp.org/cpd.

### 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.

• 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.

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

#### 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

### Day 2 of 2

#### 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

Level: Intermediate

Prerequisites:

Duration: 2 days

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