Taylors Theorem: Formula, Remainder & Examples

What Is Taylors Theorem?

Taylors Theorem is the result that lets you replace a complicated function near a point with a polynomial, and then measure exactly how wrong that replacement is. If $f$ is differentiable enough near a point $a$, the theorem writes

$$f(x) = \underbrace{\sum_{k=0}^{n}\frac{f^{(k)}(a)}{k\text{ factorial}}(x-a)^k}_{\text{Taylor polynomial } P_n(x)} + R_n(x).$$

The precise hypothesis: if $f$ is $(n+1)$ times differentiable on the open interval between $a$ and $x$, and $f^{(n)}$ is continuous on the closed interval between $a$ and $x$, then the leftover $R_n(x)$ takes the Lagrange form

$$R_n(x) = \frac{f^{(n+1)}(c)}{(n+1)\text{ factorial}}(x-a)^{n+1}, \qquad \text{for some } c \text{ between } a \text{ and } x.$$

Two pieces do all the work. The first is the Taylor polynomial $P_n(x)$, the best polynomial of degree $n$ that matches $f$ and its first $n$ derivatives at $a$. The second is the remainder $R_n(x)$, the exact gap between that polynomial and the true function. The whole value of the theorem is that it does not just approximate; it hands you a formula for the error.

When the centre is $a = 0$, the expansion is called a Maclaurin series, and every example in this article uses that centre.

What Does The Remainder Term In Taylors Theorem Mean?

The remainder $R_n(x)$ is the honest part of the theorem. The Taylor polynomial is an approximation, and $R_n(x)$ is precisely what you throw away when you stop at degree $n$.

Look at the Lagrange form again next to the polynomial's terms:

$$R_n(x) = \frac{f^{(n+1)}(c)}{(n+1)\text{ factorial}}(x-a)^{n+1}.$$

It is the very next term of the polynomial, with one change: instead of the derivative at the centre $a$, you evaluate it at some mystery point $c$ sitting between $a$ and $x$. You almost never know $c$ exactly, and you do not need to. You only need a bound on how large $f^{(n+1)}$ can be across the interval. Once you cap that derivative, the factorial $(n+1)\text{ factorial}$ in the denominator and the power $(x-a)^{n+1}$ do the rest, shrinking the error fast as $n$ grows or as $x$ moves closer to $a$.

That single idea, an unknown-but-bounded $c$, is what turns a rough guess into a guaranteed estimate.

How Does Taylors Theorem Connect To Taylor Series?

A Taylor series is what you get when the polynomial never stops: let $n \to \infty$. The series equals the function at a point exactly when the leftover vanishes there:

$$f(x) = \sum_{k=0}^{\infty}\frac{f^{(k)}(a)}{k\text{ factorial}}(x-a)^k \quad \Longleftrightarrow \quad \lim_{n \to \infty} R_n(x) = 0.$$

This is the deep payoff. On its own, an infinite Taylor series is just a candidate with no guarantee it adds up to the function it came from, and Taylors Theorem supplies the check. If you can show the remainder is squeezed to zero, the series genuinely converges to $f$, and the wider machinery of a power series takes over. A function can even have a perfectly good Taylor series that converges to the wrong thing, so the remainder is not a formality; it is the difference between a valid expansion and a false one.

How Do You Use Taylors Theorem? A Worked Example

Take $f(x) = e^x$ about $a = 0$ and stop at degree $n = 3$. Every derivative of $e^x$ is $e^x$, and $e^0 = 1$, so all four coefficients equal $1$. The Taylor polynomial is

$$P_3(x) = 1 + x + \frac{x^2}{2} + \frac{x^3}{6}.$$

Step 1: Estimate $e^{0.5}$. Substitute $x = 0.5$:

$$P_3(0.5) = 1 + 0.5 + \frac{0.25}{2} + \frac{0.125}{6} = 1 + 0.5 + 0.125 + 0.0208 = 1.6458.$$

Step 2: Compare with the true value. To four decimal places $e^{0.5} = 1.6487$, so the actual error is

$$\lvert e^{0.5} - P_3(0.5)\rvert = \lvert 1.6487 - 1.6458\rvert = 0.0029.$$

Step 3: Bound the error with the remainder, before trusting it. The Lagrange remainder for $n = 3$ is

$$R_3(0.5) = \frac{f^{(4)}(c)}{4\text{ factorial}}(0.5)^4 = \frac{e^{c}}{24}(0.5)^4, \qquad 0 < c < 0.5.$$

Since $e^x$ increases and $c < 0.5$, we have $e^{c} < e^{0.5} < 2$. Using that cap, and $(0.5)^4 = 0.0625$,

$$R_3(0.5) < \frac{2}{24}(0.0625) = 0.0052.$$

The guaranteed bound is $0.0052$, and the real error $0.0029$ sits comfortably underneath it. The estimate is not just close; it is provably close, which is the whole point of the theorem.

Final answer: $e^{0.5} \approx 1.6458$, with error $0.0029$, safely inside the remainder bound $R_3(0.5) < 0.0052$.

What Are The Standard Taylor Polynomials Used With Taylors Theorem?

Most first-year work reuses a handful of expansions about $a = 0$. Each is the polynomial part of the theorem; the remainder rides along on top of the last term shown.

Table: Common Maclaurin polynomials (centre $a = 0$) and their general term.

For $\sin x$ and $\cos x$ the higher derivatives are always $\pm\sin x$ or $\pm\cos x$, so $\lvert f^{(n+1)}(c)\rvert \le 1$ everywhere. The remainder is then bounded by $\dfrac{\lvert x\rvert^{n+1}}{(n+1)\text{ factorial}}$, which drives to zero for every $x$, so those two series converge on the whole number line.

Why Does Taylors Theorem Work?

The theorem feels almost too strong the first time: how can a polynomial ever pin down its own error? The reason is that the theorem is built from tools you already trust.

• It matches derivatives at the centre. The polynomial $P_n$ is chosen so that $P_n(a) = f(a)$, $P_n'(a) = f'(a)$, and so on up to the $n$-th derivative. Agreeing on that many derivatives forces the two graphs to hug each other near $a$; the mismatch can only show up in the next derivative, which is exactly where the remainder lives.

• The remainder is a leftover slope. The Lagrange form comes from the same mean-value idea that underlies the Mean Value Theorem: somewhere in the interval there is a point $c$ whose $(n+1)$-th derivative captures the whole gap. You do not get to know where $c$ is, only that it exists.

• Geometry backs the algebra. Near the centre the curve and the polynomial share position, slope, and bend, so they look like the same graph; far from the centre the shared information runs out and the gap widens. The factor $(x-a)^{n+1}$ in the remainder is the algebra of that widening.

Read together, these say the polynomial is the best local imitation of $f$, and the remainder is the precise cost of imitating instead of computing.

Is Taylors Theorem Just The Mean Value Theorem?

For the smallest case, yes. Set $n = 0$. The Taylor polynomial keeps only the constant term $f(a)$, and the remainder becomes

$$f(x) = f(a) + \frac{f'(c)}{1\text{ factorial}}(x-a)^1 = f(a) + f'(c)(x-a).$$

Rearranged, that is $f'(c) = \dfrac{f(x) - f(a)}{x - a}$, which is exactly the Mean Value Theorem: somewhere between $a$ and $x$ the instantaneous slope equals the average slope. So Taylors Theorem is the Mean Value Theorem generalised to higher derivatives. The MVT controls the error of a constant approximation; the full theorem controls the error of a degree-$n$ polynomial one. Recognising this connection also explains where the mystery point $c$ comes from: it is the same existence guarantee, extended.

Who Discovered Taylors Theorem?

The formula carries one name, but the pieces arrived across two centuries and several countries.

Two figures anchor the story:

• Brook Taylor (1685–1731, England) published the polynomial expansion in 1715, building on earlier work by Gregory and Newton.

• Joseph-Louis Lagrange (1736–1813, born in Italy, worked in Prussia and France) gave the remainder its usable closed form, turning an approximation into a bounded one.

Where Is Taylors Theorem Used In The Real World?

Bounding the error of a simple stand-in is a daily need across science and engineering, and this theorem is the tool that does it.

• Calculators and computers: functions like $e^x$, $\sin x$, and $\ln x$ have no finite formula, so hardware evaluates a Taylor polynomial and uses the remainder to decide how many terms guarantee the displayed digits.

• Physics approximations: the small-angle rule $\sin\theta \approx \theta$ and the pendulum, optics, and relativity approximations are all first- or second-degree Taylor polynomials, and the remainder says when the shortcut is safe.

• Numerical methods: the error of numerical integration, root-finding, and differential-equation solvers is analysed by expanding the step and reading off the remainder's power of the step size.

• Engineering and control: linearising a nonlinear system around an operating point is a degree-1 Taylor expansion, and the remainder measures how far you can drift before the linear model breaks.

• Finance and statistics: option-pricing sensitivities and risk models expand a payoff or a likelihood to second order, using the remainder to gauge the approximation.

One theorem lets every field trade an intractable function for a polynomial and still keep a receipt for the error.

What Are The Most Common Mistakes With Taylors Theorem?

These three errors account for most lost marks, and each matches a question real students ask on r/calculus, r/learnmath, and course remainder handouts.

Treating the Taylor polynomial as exact and dropping the remainder.

Where it slips in:

A student writes $e^{0.5} = 1 + 0.5 + \frac{0.25}{2} + \frac{0.125}{6}$ with an equals sign and reports it as the true value.

Don't do this:

Do not replace $f(x)$ with $P_n(x)$ using "$=$". The polynomial is an approximation, and a function need not equal a finite piece of its own series.

The correct way:

Keep the remainder: $f(x) = P_n(x) + R_n(x)$. Write "$\approx$" for the estimate and quote the bound on $R_n(x)$ alongside it.

Using the wrong factorial or power in the remainder.

Where it slips in:

A student writes the degree-$n$ remainder with $n\text{ factorial}$ and $(x-a)^{n}$, copying the last polynomial term instead of the next one.

Don't do this:

Do not reuse the degree-$n$ term's factorial and power. The remainder is the next term.

The correct way:

Use $R_n(x) = \dfrac{f^{(n+1)}(c)}{(n+1)\text{ factorial}}(x-a)^{n+1}$: the derivative order, the factorial, and the power all step up to $n+1$.

Trying to solve for $c$.

Where it slips in:

A student sets up the remainder and then tries to find the exact value of $c$ to compute the error precisely.

Don't do this:

Do not treat $c$ as a solvable unknown. The theorem only promises that some such $c$ exists in the interval.

The correct way:

Bound $\lvert f^{(n+1)}\rvert$ over the whole interval by its largest value, and use that cap. That gives a guaranteed upper bound on the error without ever locating $c$.

Practice Problems On Taylors Theorem

Work each one, then check against the answer. Answers are verified.

1. Write the degree-2 Taylor polynomial of $f(x) = e^x$ about $a = 0$.
   (Answer: $1 + x + \dfrac{x^2}{2}$.)

2. Write the degree-3 Taylor polynomial of $\cos x$ about $a = 0$.
   (Answer: $1 - \dfrac{x^2}{2}$, since the $x^3$ coefficient is $0$.)

3. Use the degree-1 Taylor polynomial of $f(x) = \sqrt{x}$ about $a = 4$ to estimate $\sqrt{4.2}$.
   (Answer: $P_1(x) = 2 + \tfrac{1}{4}(x-4)$, so $\sqrt{4.2} \approx 2.05$; true value $2.0494$.)

4. State the Lagrange remainder $R_2(x)$ for a degree-2 expansion about $a$.
   (Answer: $R_2(x) = \dfrac{f^{(3)}(c)}{6}(x-a)^3$ for some $c$ between $a$ and $x$.)

5. For $f(x) = e^x$ about $a = 0$ at degree $3$, bound the error at $x = 1$ using $e^{c} < 3$.
   (Answer: $R_3(1) < \dfrac{3}{24} = 0.125$; the true error is about $0.0516$.)

6. Using the $n = 0$ case for $f(x) = x^2$ on the interval from $a = 0$ to $x = 2$, find the point $c$ the theorem guarantees.
   (Answer: $f'(c) = \dfrac{4 - 0}{2} = 2$, and $f'(c) = 2c$, so $c = 1$.)

Where Should You Go Next After Taylors Theorem?

The theorem sits at the join between polynomials and infinite series, and several natural doors open from here.

1. Taylor series. Let the degree run to infinity and study when the remainder vanishes, so the series equals the function.

2. Taylor polynomials. Drill the polynomial half on its own, from choosing the centre to matching derivatives.

3. The Mean Value Theorem. Revisit the $n = 0$ case and the existence argument that produces the point $c$.

If your child is meeting Taylors Theorem for the first time, a live Bhanzu trainer teaches it from the approximation-plus-error picture up, so the remainder feels like the point rather than an afterthought, in the Bhanzu math program.