|Day 1 of 2|
|Importing and Organizing Data|
Objective: Understand the import methods and data types available in MATLAB and Statistics and Machine Learning Toolbox to bring data into MATLAB and organize it for analysis. Perform common tasks, such as merging data and dealing with missing data.
Objective: Perform basic statistical investigation of a data set, including visualization and calculation of summary statistics.
Objective: Use the functionality in Statistics and Machine Learning Toolbox to investigate different probability distributions and to fit distributions to a data set.
Objective: Use Statistics and Machine Learning Toolbox to 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.
|Day 2 of 2|
|Analysis of Variance|
Objective: Use Statistics and Machine Learning Toolbox functions to compare the sample means of multiple groups and to find statistically significant differences between groups.
Objective: Perform predictive modeling by fitting linear and nonlinear models to a data set. Explore techniques for improving model quality.
|Working with Multiple Dimensions|
Objective: Become familiar with techniques for reducing the dimensionality of a data set, and perform classification of categorical responses.
|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.