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QuickStart Samples

# Simple Regression QuickStart Sample (C#)

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

View this sample in: Visual Basic F# IronPython

``````using System;

using Extreme.DataAnalysis;
using Extreme.Mathematics;
using Extreme.Statistics;

namespace Extreme.Numerics.QuickStart.CSharp
{
/// <summary>
/// Illustrates the use of the SimpleRegressionModel class
/// to perform multiple linear regression.
/// </summary>
class SimpleRegression
{
static void Main(string[] args)
{
// Simple linear regression can be performed using
// the SimpleRegressionModel class. There are some special constructors
// for simple linear regression, with only one independent variable.
//
// This QuickStart sample uses data from the National Institute
// for Standards and Technology's Statistical Reference Datasets
// library at http://www.itl.nist.gov/div898/strd/.

// Note that, due to round-off error, the results here will not be exactly
// the same as the NIST results, which were calculated using 500 digits
// of precision!

// Model 1 uses the 'NoInt1' dataset. The model has no intercept.

// First, we construct Double arrays containing the data for
// the dependent and independent variables.
double[] yData1 = {130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140};
double[] xData1 = {60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70};

// Next, we create the regression model. We can pass the data arrays directly.
SimpleRegressionModel model1 = new SimpleRegressionModel(yData1, xData1);
model1.NoIntercept = true;
model1.Fit();

foreach(var parameter in model1.Parameters)
Console.WriteLine(parameter.ToString());
Console.WriteLine("Residual standard error: {0:F2}", model1.StandardError);
Console.WriteLine("R-Squared: {0:F3}", model1.RSquared);
Console.WriteLine("F-statistic: {0:F3}", model1.FStatistic);

Console.WriteLine(model1.AnovaTable.ToString());

// Model 2 uses the 'Norris' dataset.

Console.WriteLine("\n\nModel 2");
var dependent2 = Vector.Create(new double[] {
0.1, 338.8, 118.1, 888.0, 9.2, 228.1, 668.5, 998.5,
449.1, 778.9, 559.2, 0.3, 0.1, 778.1, 668.8, 339.3,
448.9, 10.8, 557.7, 228.3, 998.0, 888.8, 119.6, 0.3,
0.6, 557.6, 339.3, 888.0, 998.5, 778.9,  10.2 , 117.6,
228.9, 668.4, 449.2,   0.2});
var independent2 = Vector.Create(new double[] {
0.2, 337.4, 118.2, 884.6, 10.1, 226.5, 666.3, 996.3,
448.6, 777.0, 558.2, 0.4, 0.6, 775.5, 666.9, 338.0,
447.5, 11.6, 556.0, 228.1, 995.8, 887.6, 120.2, 0.3,
0.3, 556.8, 339.1, 887.2, 999.0, 779.0, 11.1, 118.3,
229.2, 669.1, 448.9, 0.5});

// Next, we create the regression model, using the NumericalVariable objects
// we just created:
SimpleRegressionModel model2 = new SimpleRegressionModel(dependent2, independent2);
model2.Fit();

foreach(var parameter in model2.Parameters)
Console.WriteLine(parameter.ToString());
Console.WriteLine("Residual standard error: {0:F8}", model2.StandardError);
Console.WriteLine("R-Squared: {0:F8}", model2.RSquared);