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.
- 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
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
Objective: Perform basic statistical investigation of a data set, including visualization and calculation of summary statistics.
- Central tendency
- Grouped data
Objective: Investigate different probability distributions and fit distributions to a data set.
- Probability distributions
- Distribution parameters
- Comparing and fitting distributions
- Nonparametric fitting
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
- Nonnormal ANOVA
- Categorical correlations
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