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 descriptive statistics on a data set, including visualization and calculation of summary statistics.

• Visualizing data
• Calculating parameters of location, spread, and shape
• Computing correlation coefficients
• Perform calculations with grouped data

Distributions

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

• Probability distributions and their parameters
• Comparing and fitting distributions
• Fitting nonparametric distributions
• Bootstrapping and simulation
• Generating random numbers from arbitrary distributions
• Controlling the random number stream

Day 2 of 2

Hypothesis Tests

Objective: Determine if a data set sufficiently supports a particular hypothesis. Apply hypothesis tests for common uses, such as comparing the location and spread parameters of two distributions.

• Confirmatory data analysis
• Hypothesis tests for normal distributions
• Hypothesis tests for nonnormal distributions

Analysis of Variance

Objective: Compare the sample means of multiple groups and find statistically significant differences between groups.

• Performing Analysis of Variance (ANOVA)
• Computing corrections for multiple comparisons
• Performing N-way ANOVA and Multivariate Analysis of Variance (MANOVA)
• ANOVA for non-normal data
• Independence tests for categorical data

Regression

Objective: Perform predictive modeling by fitting linear and nonlinear models to a data set. Explore techniques for improving model quality. Simplify high-dimensional data sets by reducing the dimensionality.

• Linear regression models
• Fitting linear models to data
• Evaluating the fit and adjusting the model
• Logistic and generalized linear regression
• Nonlinear regression
• Feature selection and transformation