# Statistics in IronPython

## Probability Distributions

### Discrete Distributions

Illustrates how to use the classes that represent discrete probability distributions in the Numerics.NET.Statistics.Distributions namespace.

### Continuous Distributions

Illustrates how to use the classes that represent continuous probability distributions in the Numerics.NET.Statistics.Distributions namespace.

## Analysis of Variance

### One-Way Anova

Illustrates how to use the OneWayAnovaModel class to perform a one-way analysis of variance.

### Repeated Measures Anova

Illustrates how to use the OneWayRAnovaModel class to perform a one-way analysis of variance with repeated measures.

### Two-Way Anova

Illustrates how to use the TwoWayAnovaModel class to perform a two-way analysis of variance.

## Regression Analysis

### Simple Regression

Illustrates how to perform a simple linear regression using the SimpleRegressionModel class.

### Multiple Linear Regression

Illustrates how to use the LinearRegressionModel class to perform a multiple linear regression.

### Polynomial Regression

Illustrates how to fit data to polynomials using the PolynomialRegressionModel class.

### Logistic Regression

Illustrates how to use the LogisticRegressionModel class to create logistic regression models.

### Generalized Linear Models

Illustrates how to use the GeneralizedLinearModel class to compute probit, Poisson and similar regression models.

## Time Series Analysis

### Simple Time Series

Illustrates how to perform simple operations on time series data using classes in the Numerics.NET.Statistics.TimeSeriesAnalysis namespace.

### Variable Transformations

Illustrates how to perform a range of transformations on statistical data.

### ARIMA Models

Illustrates how to work with ARIMA time series models using classes in the Numerics.NET.Statistics.TimeSeriesAnalysis namespace.

## Multivariate Analysis

### Cluster Analysis

Illustrates how to use the classes in the Numerics.NET.Statistics.Multivariate namespace to perform hierarchical clustering and K-means clustering.

### Principal Component Analysis (PCA)

Illustrates how to perform a Principal Component Analysis using classes in the Numerics.NET.Statistics.Multivariate namespace.

### Factor Analysis (FA)

Illustrates how to perform a Factor Analysis using classes in the Numerics.NET.Statistics.Multivariate namespace.

## Hypothesis Tests

### Mean Tests

Illustrates how to use various tests for the mean of one or more sanples using classes in the Numerics.NET.Statistics.Tests namespace.

### Variance Tests

Illustrates how to perform hypothesis tests involving the standard deviation or variance using classes in our .NET statistical library.

### Goodness-Of-Fit Tests

Illustrates how to test for goodness-of-fit using classes in the Numerics.NET.Statistics.Tests namespace.

### Homogeneity Of Variances Tests

Illustrates how to test a collection of variables for equal variances using classes in the Numerics.NET.Statistics.Tests namespace.

### Non-Parametric Tests

Illustrates how to perform non-parametric tests like the Wilcoxon-Mann-Whitney test and the Kruskal-Wallis test.