Statistical Methods in MATLAB
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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 yourcredit tracker.
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
- Spread
- Shape
- Correlations
- Grouped data
Distributions
Objective:Investigate different probability distributions and fit distributions to a data set.
- Probability distributions
- 分布参数
- 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
- Adjusting the model
- 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:
- MATLAB Fundamentalsand knowledge of basic Statistics and Machine Learning Toolbox
Duration:2 days
Languages:English, Français, 日本語