## Types in MathNet.Numerics.LinearRegression

Type SimpleRegression

Namespace MathNet.Numerics.LinearRegression

### Public Static Functions

#### ValueTuple<double, double>Fit(Double[] x, Double[] y)

Least-Squares fitting the points (x,y) to a line y : x -> a+b*x, returning its best fitting parameters as (a, b) tuple, where a is the intercept and b the slope.
##### Parameters
###### `Double[]` x

Predictor (independent)

###### `Double[]` y

Response (dependent)

#### ValueTuple<double, double>Fit(IEnumerable<Tuple<double, double>> samples)

Least-Squares fitting the points (x,y) to a line y : x -> a+b*x, returning its best fitting parameters as (a, b) tuple, where a is the intercept and b the slope.
##### Parameters
###### `IEnumerable<Tuple<double, double>>` samples

Predictor-Response samples as tuples

#### doubleFitThroughOrigin(Double[] x, Double[] y)

Least-Squares fitting the points (x,y) to a line y : x -> b*x, returning its best fitting parameter b, where the intercept is zero and b the slope.
##### Parameters
###### `Double[]` x

Predictor (independent)

###### `Double[]` y

Response (dependent)

#### doubleFitThroughOrigin(IEnumerable<Tuple<double, double>> samples)

Least-Squares fitting the points (x,y) to a line y : x -> b*x, returning its best fitting parameter b, where the intercept is zero and b the slope.
##### Parameters
###### `IEnumerable<Tuple<double, double>>` samples

Predictor-Response samples as tuples