Linear Equation

linear_equation(first_constant, second_constant, precision=4)

Generates a linear function to provide evaluations at variable inputs

Parameters

• first_constant (int or float) – Coefficient of the linear term of the resultant linear function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)

• second_constant (int or float) – Coefficient of the constant term of the resultant linear function; if zero, it will be converted to a small, non-zero decimal value (e.g., 0.0001)

• precision (int, default=4) – Maximum number of digits that can appear after the decimal place of the resultant roots

Raises

• TypeError – First two arguments must be integers or floats

• ValueError – Last argument must be a positive integer

Returns

evaluation – Function for evaluating a linear equation when passed any integer or float argument

Return type

func

See also

linear_derivatives(), linear_integral(), linear_roots(), linear_model()

Notes

• Standard form of a linear function: \(f(x) = a\cdot{x} + b\)

• Linear Functions

Examples

Import linear_equation function from regressions library

>>> from regressions.analyses.equations.linear import linear_equation

Create a linear function with coefficients 2 and 3, then evaluate it at 10

>>> evaluation_first = linear_equation(2, 3)
>>> print(evaluation_first(10))
23.0

Create a linear function with coefficients -2 and 3, then evaluate it at 10

>>> evaluation_second = linear_equation(-2, 3)
>>> print(evaluation_second(10))
-17.0

Create a linear function with all inputs set to 0, then evaluate it at 10

>>> evaluation_zero = linear_equation(0, 0)
>>> print(evaluation_zero(10))
0.0011