Examples

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Use this list to find examples in the documentation.

Organizing Data

Using Categorical Arrays
Using Dataset Arrays
Using Grouping Variables

Descriptive Statistics

Measures of Central Tendency
Measures of Dispersion
Measures of Shape
Resampling Statistics

Statistical Visualization

Scatter Plots
Box Plots
Normal Probability Plots
Quantile-Quantile Plots
Cumulative Distribution Plots
Other Probability Plots

Probability Distributions

Probability Density Functions
Cumulative Distribution Functions
Inverse Cumulative Distribution Functions
Distribution Statistics Functions
Distribution Fitting Functions
Negative Log-Likelihood Functions
Random Number Generators
Copulas: Generate Correlated Samples
Common Generation Methods
Using Scramble, Leap, and Skip
Generate a Quasi-Random Point Set
Generate a Quasi-Random Stream
Generating Data Using the Pearson System
Generating Data Using the Johnson System

Multivariate Methods

Gaussian Mixture Models
Multidimensional Scaling
Sequential Feature Selection
Nonnegative Matrix Factorization
Principal Component Analysis
Factor Analysis

Hypothesis Tests

Hypothesis Testing
Kruskal-Wallis Test
Friedman's Test

Analysis of Variance

One-Way ANOVA
Multiple Comparisons
Two-Way ANOVA
N-Way ANOVA with a Small Data Set
N-Way ANOVA with a Large Data Set
ANOVA with Random Effects
Analysis of Covariance
ANOVA with Multiple Responses

Regression Analysis

ANOVA
Diagnostic Plots
Residuals — Model Quality for Training Data
Plots to Understand Predictor Effects
Plots to Understand Terms Effects
Change Models
Linear Regression Workflow
Compare large and small stepwise models
Compare Robust Regression to a Standard Least-Squares Fit
Ridge Regression
Lasso Regularization
Lasso and Elastic Net Regularization with Cross Validation
Wide Data via Lasso and Parallel Computing
Partial Least Squares
Residuals — Model Quality for Training Data
Plots to Understand Predictor Effects and How to Modify a Model
Generalized Linear Regression Workflow
Regularize Poisson Regression
Regularize Logistic Regression
Regularize Wide Data in Parallel
Examine Quality and Adjust the Fitted Model
Nonlinear Regression Workflow
Mixed-Effects Models Using nlmefit and nlmefitsa
Examining Residuals for Model Verification
Cross Validating a Regression Tree
Creating Regression Trees Using classregtree
Regularizing a Regression Ensemble
Workflow Example: Regression of Insurance Risk Rating for Car Imports with TreeBagger

Cluster Analysis

Hierarchical Clustering
K-Means Clustering
Gaussian Mixture Models

Classification

Create Discriminant Analysis Classifiers
Creating and Visualizing a Discriminant Analysis Classifier
Singular Covariance Matrix
Resubstitution Error of a Discriminant Analysis Classifier
Bartlett Test for Equal Covariance Matrices
Check Q-Q Plots for Linear and Quadratic Discriminants
Mardia Kurtosis Test for Linear and Quadratic Discriminants
Classifying Query Data Using knnsearch
Resubstitution Error of a Classification Tree
Selecting Appropriate Tree Depth
Pruning a Classification Tree
Creating Classification Trees Using classregtree
Test Ensemble Quality
Unequal Classification Costs
Classification with Many Categorical Levels
Surrogate Splits
Tuning RobustBoost
Random Subspace Classification
Workflow Example: Classifying Radar Returns for Ionosphere Data with TreeBagger

Markov Models

Markov Chains
Hidden Markov Models (HMM)

Design of Experiments

Full Factorial Designs
Fractional Factorial Designs
Response Surface Designs
D-Optimal Designs

Statistical Process Control

Control Charts
Capability Studies

Parallel Computing

Parallel Treebagger
Parallel Jackknife
Parallel Cross Validation
Parallel Bootstrap

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