The theory is called linear regression and allows the statistician to determine the slope and the y-intercept of the so-called "line of best fit." It also provides for the calculation of a number called the "correlation coefficient" that tells how close this best fit line actually comes to "capturing" the trend of the data.
For instance, to approximate the value of a non-linear function at a given point on its curve, one can use the linearfunction which is tangent to the curve at a nearby known point.
Linearfunctions always have as domain the set of all real numbers and a range of all real numbers.
In mathematics, linear programming (LP) problems are optimization problems in which the objective function and the constraints are all linear.
Geometrically, the linear constraints define a convex polyhedron, which is called the feasible region.
In contrast to linear programming, which can be solved efficiently in the worst case, integer programming problems are in the worst case undecidable, and in many practical situations (those with bounded variables) NP-hard.