# Linear

Calculates the linear regression parameters and evaluates the regression line at arbitrary abscissas

Controller: **CodeCogs**

## Interface

C++

HTML

## Class Linear

Linear regression is a method to best fit a linear equation (straight line) of the form to a collection of points , where is the slope and the intercept on the axis. The algorithm basically requires minimisation of the sum of the squared distance from the data points to the proposed line. This is achieved by calculating the derivative with respect to a and b and setting these to zero. Let us define the following Then the slope is the intercept on the Y axis Below you will find the regression graph for a set of arbitrary points, which were also used in the forthcoming example. The regression line, displayed in red, has been calculated using this class.### Example 1

- The following example displays the slope, Y intercept and regression coefficient
for a certain set of 7 points.
#include <codecogs/maths/approximation/regression/linear.h> #include <iostream> #include <iomanip> using namespace std; int main() { double x[7] = { 1.5, 2.4, 3.2, 4.8, 5.0, 7.0, 8.43 }; double y[7] = { 3.5, 5.3, 7.7, 6.2, 11.0, 9.5, 10.27 }; Maths::Regression::Linear A(7, x, y); cout << " Slope = " << A.getSlope() << endl; cout << "Intercept = " << A.getIntercept() << endl << endl; cout << "Regression coefficient = " << A.getCoefficient() << endl; cout << endl << "Regression line values" << endl << endl; for (double i = 0.0; i <= 3; i += 0.6) { cout << "x = " << setw(3) << i << " y = " << A.getValue(i); cout << endl; } return 0; }

Output:Slope = 0.904273 Intercept = 3.46212 Regression coefficient = 0.808257 Regression line values x = 0 y = 3.46212 x = 0.6 y = 4.00469 x = 1.2 y = 4.54725 x = 1.8 y = 5.08981 x = 2.4 y = 5.63238 x = 3 y = 6.17494

### Authors

*Lucian Bentea (August 2005)*

##### Source Code

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## Members of Linear

#### Linear

Initializes the class by calculating the slope, intercept and regression coefficient based on the given constructor arguments.Linear( int `n`double* `x`double* `y`) *[constructor]*### Note

- The slope should not be infinite.

n The number of initial points in the arrays *x*and*y*x The x-coordinates of points y The y-coordinates of points

#### GetValue

doublegetValue( double `x`) x the abscissa used to evaluate the linear regression function

#### GetCoefficient

The regression coefficient indicated how well linear regression fits to the original data. It is an expression of error in the fitting and is defined as: This varies from 0 (no linear trend) to 1 (perfect linear fit). If and , thendoublegetCoefficient( ) *r*is considered to be equal to 1.

## Linear Once

doubleLinear_once( | int | n | |

double* | x | ||

double* | y | ||

double | a | ) |

This function implements the Linear class for one off calculations, thereby avoid the need to instantiate the Linear class yourself.

### Example 2

- The following graph fits a straight line to the following values:
x = 1 y = 0.22 x = 2 y = 0.04 x = 3 y = -0.13 x = 4 y = -0.17 x = 5 y = -0.04 x = 6 y = 0.09 x = 7 y = 0.11

### Parameters

n The number of initial points in the arrays *x*and*y*x The x-coordinates of points y The y-coordinates of points a The x-coordinate for the output location

### Returns

- the interpolated y-coordinate that corresponds to
*a*.

##### Source Code

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