Multiple Linear Regression in C# QuickStart Sample

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

This sample is also available in: Visual Basic, F#, IronPython.

Overview

This QuickStart sample demonstrates how to perform multiple linear regression analysis using the LinearRegressionModel class in Numerics.NET.

The sample uses a dataset containing test scores of 200 high school students to build a regression model predicting science scores based on math, reading, and social studies scores along with gender. It shows:

• How to load data from a CSV file into a DataFrame
• Creating regression models using both explicit variable lists and formula notation
• Fitting the model and accessing regression parameters
• Getting parameter estimates, standard errors, t-statistics and p-values
• Calculating confidence intervals for parameters
• Accessing model statistics like R-squared and F-statistics
• Generating ANOVA tables and model summaries
• Working with model parameters both by index and by name

The sample demonstrates both basic model building and more advanced statistical analysis techniques, making it useful for both beginners and those needing more sophisticated regression analysis.

The code

using System;

using Numerics.NET.DataAnalysis;
using Numerics.NET;
using Numerics.NET.Statistics;
using Numerics.NET.Data.Text;

// Illustrates building multiple linear regression models using
// the LinearRegressionModel class in the
// Numerics.NET.Statistics namespace of Numerics.NET.

// The license is verified at runtime. We're using
// a 30 day trial key here. For more information, see
//     https://numerics.net/trial-key
Numerics.NET.License.Verify("your-trial-key-here");

// Multiple linear regression can be performed using
// the LinearRegressionModel class.
//
//

// This QuickStart sample uses data test scores of 200 high school
// students, including science, math, and reading.

// First, read the data from a file into a data frame.
var data = DelimitedTextFile.ReadDataFrame(@"..\..\..\..\..\Data\hsb2.csv");

// Now create the regression model. Parameters are the data frame,
// the name of the dependent variable, and a string array containing
// the names of the independent variables.
var model = new LinearRegressionModel(data,
    "science", new string[] {"math", "female", "socst", "read"});

// Alternatively, we can use a formula to describe the variables
// in the model. The dependent variable goes on the left, the
// independent variables on the right of the ~:
var model2 = new LinearRegressionModel(data,
    "science ~ math + female + socst + read");

// We can set model options now, such as whether to exclude
// the constant term:
// model.NoIntercept = false;

// The Fit method performs the actual regression analysis.
model.Fit();

// The Parameters collection contains information about the regression
// parameters.
Console.WriteLine("Variable              Value    Std.Error  t-stat  p-Value");
foreach(var parameter in model.Parameters)
{
    // Parameter objects have the following properties:
    Console.WriteLine("{0,-20}{1,10:F6}{2,10:F6}{3,8:F2} {4,7:F5}",
        // Name, usually the name of the variable:
        parameter.Name,
        // Estimated value of the parameter:
        parameter.Value,
        // Standard error:
        parameter.StandardError,
        // The value of the t statistic for the hypothesis that the parameter
        // is zero.
        parameter.Statistic,
        // Probability corresponding to the t statistic.
        parameter.PValue);
}
Console.WriteLine();

// In addition to these properties, Parameter objects have
// a GetConfidenceInterval method that returns
// a confidence interval at a specified confidence level.
// Notice that individual parameters can be accessed
// using their numeric index. Parameter 0 is the intercept,
// if it was included.
Interval confidenceInterval = model.Parameters[0].GetConfidenceInterval(0.95);
Console.WriteLine("95% confidence interval for intercept: {0:F4} - {1:F4}",
    confidenceInterval.LowerBound, confidenceInterval.UpperBound);

// Parameters can also be accessed by name:
confidenceInterval = model.Parameters.Get("math").GetConfidenceInterval(0.95);
Console.WriteLine("95% confidence interval for 'math': {0:F4} - {1:F4}",
    confidenceInterval.LowerBound, confidenceInterval.UpperBound);
Console.WriteLine();

// There is also a wealth of information about the analysis available
// through various properties of the LinearRegressionModel object:
Console.WriteLine($"Residual standard error: {model.StandardError:F3}");
Console.WriteLine($"R-Squared:               {model.RSquared:F4}");
Console.WriteLine($"Adjusted R-Squared:      {model.AdjustedRSquared:F4}");
Console.WriteLine($"F-statistic:             {model.FStatistic:F4}");
Console.WriteLine($"Corresponding p-value:   {model.PValue:F5}");
Console.WriteLine();

// Much of this data can be summarized in the form of an ANOVA table:
Console.WriteLine(model.AnovaTable.ToString());

// All this information can be printed using the Summarize method.
// You will also see summaries using the library in C# interactive.
Console.WriteLine(model.Summarize());

Console.Write("Press any key to exit.");
Console.ReadLine();