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

· 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

· 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