Training - Courses

Objective: Before performing any analysis it is necessary to bring data into MATLAB and organize it appropriately. This
chapter covers import methods and data types available in MATLAB and Statistics Toolbox. Dealing with common problems, such
as missing data, is highlighted.

• Importing data
• Organizing data
• Categorical data and dataset arrays
• Incommensurate and missing data

Objective: This chapter focuses on basic statistical investigation of a data set, including visualization and calculation of
summary statistics.

• Descriptive statistics
• Central tendency
• Spread
• Statistical visualization
• Grouped data

Objective: This chapter highlights the functionality available in Statistics Toolbox for investigating different probability
distributions, as well as generating random numbers from either one of these distributions or any other distribution.

• Probability distributions
• Distributions in Statistics Toolbox
• Generating random numbers
• Random number streams
• Random numbers for arbitrary distributions
• Monte Carlo simulation

Objective: After exploring a data set, it is often desirable to compare the data to a theoretical distribution, either to
estimate parameters of the data's distribution, or to test a hypothesis about the data. This chapter demonstrates how to
achieve these tasks with the functionality available in Statistics Toolbox.

• Choosing a distribution
• Fitting a distribution
• Testing a distribution
• Hypothesis testing
• Example: gasoline prices

Objective: This chapter discusses how to fit linear and nonlinear models to a bivariate data set.

• Predictors and responses
• Scatter plots
• Correlation and covariance
• Linear models
• Nonlinear models

Objective: This chapter considers the problem of determining differences in grouped data, including multiple comparisons
between groups. Multiple groupings and multiple response variables are discussed.

• One-way ANOVA
• Two-way ANOVA
• N-way ANOVA
• Multivariate ANOVA

Objective: This chapter extends the concepts of the previous chapters to data sets with many variables. Specialized
techniques for multivariate analysis and visualization are introduced.

• Multivariate plotting
• Principal component analysis
• Clustering