- 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 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
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
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
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