public class LinearRegressionModel : RegressionModel

Public Class LinearRegressionModel
	Inherits RegressionModel

public ref class LinearRegressionModel : public RegressionModel

type LinearRegressionModel =  
    class
        inherit RegressionModel
    end

The LinearRegressionModel type exposes the following members.

	Name	Description
	LinearRegressionModel(IDataFrame, String)	Constructs a new LinearRegressionModel.
	LinearRegressionModel(NumericalVariable, NumericalVariable)	Constructs a new LinearRegressionModel.
	LinearRegressionModel(VectorDouble, VectorDouble)	Constructs a new LinearRegressionModel.
	LinearRegressionModel(Vector, Matrix)	Constructs a new SimpleRegressionModel.
	LinearRegressionModel(DataTable, String, String)	Obsolete. Constructs a new LinearRegressionModel.
	LinearRegressionModel(IDataFrame, String, String)	Constructs a new LinearRegressionModel.
	LinearRegressionModel(IDataFrame, String, String)	Constructs a new LinearRegressionModel.
	LinearRegressionModel(IDataFrame, String, String)	Constructs a new LinearRegressionModel.
	LinearRegressionModel(VariableCollection, String, String)	Constructs a new LinearRegressionModel.
	LinearRegressionModel(VectorDouble, MatrixDouble, Boolean, VectorDouble)	Constructs a new LinearRegressionModel.
	LinearRegressionModel(IDataFrame, String, String, Boolean, String)	Constructs a new LinearRegressionModel.
	LinearRegressionModel(ModelInput, VectorDouble, SymmetricMatrixDouble, Int32, Double)	Constructs a fitted linear regression model.

	Name	Description
	AdjustedRSquared	Gets the adjusted R Squared value for the regression. (Inherited from UnivariateModel.)
	AnovaTable	Gets the AnovaTable that summarizes the results of this model. (Inherited from UnivariateModel.)
	BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model.)
	BestFitParameters	Gets a vector containing the values of the regression parameters.
	CoefficientOfVariation	Gets the coefficient of variation for the regression.
	Computed	Gets whether the regression model has been computed. (Inherited from Model.)
	CovarianceMatrix	Gets the covariance matrix of the model parameters. (Inherited from RegressionModel.)
	Data	Gets an object that contains all the data used as input to the model. (Inherited from Model.)
	DegreesOfFreedom	Gets the total degrees of freedom of the data. (Inherited from UnivariateModel.)
	DependentVariable	Gets a vector that contains the dependent variable that is to be fitted. (Inherited from RegressionModel.)
	DependentVariable	Gets the dependent variable for the regression model.
	DependentVariables	Gets the collection of dependent variables associated with this model. (Inherited from Model.)
	FStatistic	Gets the F statistic for the regression. (Inherited from UnivariateModel.)
	IndependentVariables	Gets a matrix whose columns contain the independent variables in the model. (Inherited from RegressionModel.)
	IndependentVariables	Gets the collection of independent variables associated with this model. (Inherited from Model.)
	InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model.)
	InterceptParameterName	Gets or sets the name of the intercept parameter.
	Leverage	Returns the leverage of each observation.
	LogLikelihood	Gets the log-likelihood that the model generated the data. (Inherited from RegressionModel.)
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model.)
	ModelSchema	Gets the collection of variables used in the model. (Inherited from Model.)
	ModelSubset	Returns an array of indexes of final model features in the set of input features. (Inherited from Model.)
	NoIntercept	Gets or sets whether to include the intercept or constant term in the regression model.
	NumberOfObservations	Gets the number of observations the model is based on. (Inherited from UnivariateModel.)
	ObservationMatrix	Gets or sets a matrix whose rows contain the observations.
	ParallelOptions	Gets or sets an object that specifies how the calculation of the model should be parallelized. (Inherited from Model.)
	Parameters	Gets the collection of parameters associated with this model. (Inherited from RegressionModel.)
	Parameters	Gets the collection of parameters associated with this model. (Inherited from UnivariateModel.)
	ParameterValues	Gets the values of the parameters associated with this model. (Inherited from RegressionModel.)
	PredictedValues	Gets a vector containing the model's predicted values for the dependent variable.
	Predictions	Gets a vector containing the model's predicted values for the dependent variable. (Inherited from RegressionModel.)
	PValue	Gets the probability corresponding to the F statistic for the regression. (Inherited from UnivariateModel.)
	Residuals	Gets a vector containing the residuals of the model. (Inherited from RegressionModel.)
	Residuals	Gets a vector containing the residuals of the model.
	ResidualSumOfSquares	Gets the sum of squares of the residuals of the model. (Inherited from UnivariateModel.)
	RidgeParameter	Gets or sets the coefficient of the squared norm of the regression parameters for ridge regression.
	RSquared	Gets the R Squared value for the regression. (Inherited from UnivariateModel.)
	StandardError	Gets the standard error of the regression. (Inherited from UnivariateModel.)
	Standardize	Obsolete. Gets or sets whether the variables should be standardized prior to computing the regression.
	Status	Gets the status of the model, which determines which information is available. (Inherited from Model.)
	StepwiseOptions	Gets or sets an object that specifies options for performing stepwise regression.
	SupportsWeights	Indicates whether the model supports case weights. (Overrides ModelSupportsWeights.)
	VarianceInflationFactors	Returns the Variance Inflation Factor (VIF) for each variable in the model.
	Weights	Gets or sets the actual weights. (Inherited from Model.)
	WeightVector	Gets or sets the weights of the observations in this model.

	Name	Description
	Compute	Computes the model. (Inherited from Model.)
	Compute(ParallelOptions)	Computes the model. (Inherited from Model.)
	ComputeModel	Computes the model. (Inherited from Model.)
	ComputeModel(ParallelOptions)	Fits the model to the data. (Overrides ModelComputeModel(ParallelOptions).)
	ComputeModel(ModelInput, ParallelOptions)	Fits the model to the data. (Overrides ModelComputeModel(ModelInput, ParallelOptions).)
	Contains(RegressionModel)	Returns whether another RegressionModel is nested within this instance. (Inherited from RegressionModel.)
	Contains(UnivariateModel)	Returns whether another UnivariateModel is nested within this instance. (Inherited from Model.)
	Equals	Determines whether the specified Object is equal to the current Object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	Fit	Fits the model to the data. (Inherited from Model.)
	Fit(ParallelOptions)	Fits the model to the data. (Inherited from Model.)
	FitCore	Computes the model to the specified input using the specified parallelization options. (Inherited from Model.)
	GetAkaikeInformationCriterion	Returns the Akaike information criterion (AIC) value for the model. (Inherited from UnivariateModel.)
	GetBayesianInformationCriterion	Returns the Bayesian information criterion (BIC) value for the model. (Inherited from UnivariateModel.)
	GetBreuschGodfreyTest	Gets the Breusch-Godfrey test for serial correlation in the residuals of the regression model.
	GetConfidenceBandwidth(VectorDouble)	Gets the width of the 95% confidence band around the best-fit curve at the specified point.
	GetConfidenceBandwidth(Vector)	Gets the width of the 95% confidence band around the best-fit curve at the specified point.
	GetConfidenceBandwidth(VectorDouble, Double)	Gets the width of the confidence band around the best-fit curve at the specified point.
	GetConfidenceBandwidth(Vector, Double)	Gets the width of the confidence band around the best-fit curve at the specified point.
	GetCooksDistance	Returns Cook's distance for each of the observations.
	GetCorrelationMatrix	Gets the correlation matrix for the independent variables in the model.
	GetCovarianceMatrix	Gets the covariance matrix for the independent variables in the model.
	GetDeletedResiduals	Returns the deleted residual for each observation
	GetDffits	Returns the DFFITS value for each of the observations.
	GetDurbinWatsonStatistic	Gets the Durbin-Watson statistic for the residuals of the regression.
	GetExternallyStudentizedResiduals	Returns the externally studentized residual for each observation.
	GetHashCode	Serves as a hash function for a particular type. (Inherited from Object.)
	GetLeverage	Returns the leverage of each observation.
	GetLogLikelihood	Gets the log-likelihood of the fitted model. (Overrides UnivariateModelGetLogLikelihood.)
	GetNormalityOfResidualsTest	Returns a test to verify that the residuals follow a normal distribution.
	GetNormalityOfResidualsTest(TestOfNormality)	Returns a test to verify that the residuals follow a normal distribution.
	GetPredictionBandwidth(VectorDouble)	Gets the width of the prediction band around the best-fit curve at the specified point.
	GetPredictionBandwidth(Vector)	Gets the width of the prediction band around the best-fit curve at the specified point.
	GetPredictionBandwidth(VectorDouble, Double)	Gets the width of the prediction band around the best-fit curve at the specified point.
	GetPredictionBandwidth(Vector, Double)	Gets the width of the prediction band around the best-fit curve at the specified point.
	GetStudentizedDeletedResiduals	Returns the studentized deleted residual for each observation
	GetStudentizedResiduals	Returns the studentized residual for each observation.
	GetSumOfSquaresMatrix	Calculates the sum-of-squares matrix for the VariableCollection.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	GetVarianceInflationFactors	Returns the Variance Inflation Factor (VIF) for each variable in the model.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Predict(Double)	Predicts the value of the dependent variable based on the specified values of the independent variables.
	Predict(Matrix)	Predicts the values of the dependent variable for multiple collectionso of values for the independent variables.
	Predict(Vector)	Predicts the value of the dependent variable based on the specified values of the independent variables.
	Predict(IDataFrame, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from RegressionModel.)
	Predict(MatrixDouble, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from RegressionModel.)
	Predict(VectorDouble, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from RegressionModel.)
	PredictCore(MatrixDouble, Boolean, Boolean)	Predicts the value of the dependent variable based on the specified values of the features. (Overrides RegressionModelPredictCore(MatrixDouble, Boolean, Boolean).)
	PredictCore(VectorDouble, Boolean, Boolean)	Predicts the value of the dependent variable based on the specified values of the features. (Overrides RegressionModelPredictCore(VectorDouble, Boolean, Boolean).)
	ResetComputation	Clears all computed model parameters. (Inherited from Model.)
	SetAnovaModelRow	Sets the data of a row in the model's AnovaTable. (Inherited from UnivariateModel.)
	SetAnovaRow	Sets the data of a row in the model's AnovaTable. (Inherited from UnivariateModel.)
	SetDataSource	Uses the specified data frame as the source for all input variables. (Inherited from Model.)
	Summarize	Returns a string containing a human-readable summary of the object using default options. (Inherited from Model.)
	Summarize(SummaryOptions)	Returns a string containing a human-readable summary of the object using the specified options. (Overrides ModelSummarize(SummaryOptions).)
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Model.)

Use the LinearRegressionModel class to analyze a linear relationship between two or more numerical variables. A multiple linear regression model tries to express one variable, called the dependent variable, as a linear combination of one or more other variables called independent variables or predictors.

Two derived classes provide convenient interfaces for specific kinds of regression.

• The SimpleRegressionModel class represents a linear regression model with one independent variable, including linearized models like exponential and logarithmic regression.
• The PolynomialRegressionModel class represents a linear regression model where the independent variables are all powers of the same variable.

Numerical Libraries

Reference