Answers · The mathematics underneath
How do you find the parabola that best fits a function near a point?
In shortMatch the function's height, slope and bending at the chosen point; those three conditions single out exactly one parabola.
To find it, impose three conditions at the chosen point: the parabola must have the same value, the same first rate, and the same second rate as the function there. Those three conditions determine its three coefficients, so there is one such parabola, not a family of them.
Why the half is forced
Write the parabola as . At the linear and squared terms vanish, so , which forces . Differentiate once: , so , which forces . Differentiate twice: , so . Matching the function's second rate gives , hence . The half is not a convention; it is what makes the parabola's own second rate come out equal to . Each condition also leaves the earlier ones intact, because and its first rate vanish at .
Take at . Its value, first rate and second rate are all 1 at 0, so the matching line is and the matching parabola is . At distance from 0 the true value is , the line gives , and the parabola gives .
At the parabola is 1.105 against a true 1.105171, an error of 0.000171, about one part in six thousand of the value. The line at the same distance is 1.1, an error of 0.005171, about one part in two hundred. At the parabola's error is 0.023721 and the line's is 0.148721. Halving the distance from 0.5 to 0.25 cuts the line error from 0.148721 to 0.034025, a factor of about four, and the parabola error from 0.023721 to 0.002775, a factor of about eight and a half. At the line is 2 against 2.718282 and the parabola is 2.5 against 2.718282, so even the parabola is short by 0.218282. The factors are the square and the cube of the halving, not a coincidence of these numbers.
What the natural guess misses
The natural guess is that any parabola through will do, or that a parabola can be chosen by eye once it touches the curve. Passing through the point is only one condition, and infinitely many parabolas satisfy it: in , is fixed but and are still free. Matching the slope fixes , leaving a one-parameter family of parabolas that all touch the curve with the right direction but have different bending. Only the second-rate condition fixes , and it fixes it as . A parabola with any other still passes through the point and still has the right slope, but it bends differently, so its error near the point is of the same size as the distance squared rather than the distance cubed.
The error that remains has a sharp order. For at 0 the first omitted term is , because the third rate of at 0 is 1 and , so the parabola's error is close to : at that predicts 0.0001667 against the actual 0.000171, and at it predicts 0.020833 against 0.023721. The line's first omitted term is , which predicts 0.005 at and 0.125 at . The same factorial pattern continues: the cubed term has 6 underneath, the fourth has 24. This is why adding the squared term buys so much at small distances and why the gain is local.
Where the fit stops
A polynomial fit is a statement about a neighbourhood, not a global replacement. The error of the parabola grows like the cube of the distance from , and the error of the line grows like the square, so both become useless far enough out. More terms do not always repair this. The function has value 1, first rate 0, and second rate at 0, so its matching parabola is . At the rule is and the parabola is 0; at the rule is and the parabola is . Its polynomial approximations converge only for and diverge outside that interval, however many terms are taken. The boundary is not marked on the graph of the rule.
That radius is the practical limit. Near the three-condition parabola is the best local quadratic fit because it matches the only three local data available to a quadratic; away from those data no longer describe the function, and the fit is only as good as the next term in the expansion.
Worth keeping
- Near a point, matching value, slope and bending fixes exactly one parabola.
- The parabola's error shrinks like the cube of the distance, while the tangent line's error shrinks like the square.
- A smooth function can still have polynomial fits that fail outside a finite radius.
Where this goes next
This question is one step of Change and Accumulation, a course that works through its subject from the beginning. The page that teaches this part of it is The Straight Line That Fits, Then the Parabola, and it reads the whole way through whether or not you have an account.
What an account changes is the practice: problems standing under every idea, marked with a reason rather than a tick, in a copy of the course that is yours.
Read that pageThe whole courseOther questions people ask about this
- How can there be a slope at a single point if slope needs two points?A slope at one point is the single value that slopes between nearby pairs of points settle on as the pair closes up; it is not a slope measured between two identical points.
- How can you add up infinitely many infinitely thin slices and get a finite number?You add small positive pieces, then take the finite total they approach as the pieces shrink; the pieces never all become zero before the adding.
- Why is a function thought of as a rule rather than just a formula?A function is the rule that gives exactly one output for each input; a formula is only one way of writing that rule down.
- Why do you multiply derivatives when functions are composed?Because a small change at the input is scaled once by the inner function and again by the outer function, so the two scale factors multiply.
- What is the difference between variance and standard deviation?They measure the same spread: variance is the average squared distance from the mean, standard deviation is its square root, so you report the standard deviation and calculate with the variance.
- How do you make a vague claim precise enough to tell whether it's true?Fix what is being talked about, what is asserted, which cases are covered and the cut-off, then name an observation that would prove it false.