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A twice-differentiable real-valued function on an open interval is convex if and only if its second derivative is nonnegative everywhere. If $f^{\prime \prime }\ge 0$, the mean value theorem makes $f^{\prime }$ nondecreasing, which implies the supporting-line and chord inequalities for convexity. Conversely, the slopes of a convex function are ordered; where the second derivative exists, this gives $f^{\prime \prime }\ge 0$. Strict positivity of $f^{\prime \prime }$ is sufficient for strict convexity, but is not necessary: $f(x)=x^4$ is strictly convex despite $f^{\prime \prime }(0)=0$.

For $a<b$ and a real-valued function $f$ with a finite Riemann integral or Lebesgue integral on $[a,b]$, the signed area under a graph is its definite integral: geometric area above the horizontal axis contributes positively, and geometric area below it contributes negatively. If these two nonnegative areas are $A_{+}$ and $A_{-}$, thenThe second expression is the total geometric area under a graph. Taking the absolute value of the first expression does not usually give the second: cancellation has already removed information about how much area lies on each side.^[ref]

For an exact example, take the polynomial function $f(x)=x^2-1$ on $[-2,2]$. Its polynomial roots are $-1$ and $1$: it is negative between them and positive on the two outer intervals. An antiderivative is $F(x)=x^3/3-x$, so the fundamental theorem of calculus givesThus $A_{+}=8/3$ and $A_{-}=4/3$. The signed area under a graph is $4/3$, whereas the total geometric area under a graph is $4$. To compute the latter with antiderivatives, split at each sign-changing polynomial root and reverse the sign of the negative contribution.

Cancellation is useful information when the integrand represents a signed contribution. For the polynomial function $f(x)=x$ on $[-1,1]$, the two triangular areas give ${\int }_{-1}^{1}xdx=0$, although ${\int }_{-1}^1|x|dx=1$. Zero net signed area therefore means balanced positive and negative contributions, rather than an empty region.

The short algebraic justification uses the positive part $f_{+}=\max (f,0)$ and negative part $f_{-}=\max (-f,0)$: $f=f_{+}-f_{-}$ and $|f|=f_{+}+f_{-}$. Their integrals give $A_{+}$ and $A_{-}$, and linearity of integration gives the difference and sum formulas. For Lebesgue integration, require both contributions to be finite to avoid the undefined subtraction $\infty -\infty$.^[ref]

A real-valued function $f:D\to \mathbb{R}$ is a strictly decreasing function if every pair $x<y$ in its domain of a function satisfies $f(x)>f(y)$. It is a nonincreasing function and is injective, with a strictly decreasing inverse function on its image of a function. Negating a strictly increasing function gives a strictly decreasing function: $f(x)=-x^3$ is an example on the real line, even though its derivative vanishes at zero. Equal outputs are forbidden, unlike for a general nonincreasing function.

A real-valued function $f:D\to \mathbb{R}$ is a strictly increasing function if every pair $x<y$ in its domain of a function satisfies $f(x)<f(y)$. It is therefore injective, and each output has at most one preimage. Its inverse function on its image of a function is also strictly increasing. Every strictly increasing function is a nondecreasing function; the converse fails when outputs repeat.

The polynomial $f(x)=x^3$ is a strictly increasing function on the real line, although its derivative $f^{\prime }(x)=3x^2$ is zero at zero. Indeed, for $x<y$,because $x^2+xy+y^2=(x+y/2{)}^2+3y^2/4$ can vanish only when $x=y=0$. Thus a positive derivative everywhere is sufficient on a real interval, but is not necessary for a strictly increasing function.

Strict monotonicity does not ensure continuity: $g(x)=x$ for $x<0$ and $g(x)=x+1$ for $x\ge 0$ is a strictly increasing function with a jump discontinuity. Its image of a function omits $[0,1)$, so the inverse function exists only on the actual image of a function, rather than on all the real numbers.

Every continuous real-valued function on a compact interval can be approximated by polynomials with arbitrarily small error everywhere on the interval. More precisely, if $a<b$ and $f:[a,b]\to \mathbb{R}$ is continuous, then for every $\epsilon >0$ there is a polynomial $p$ withThe error is measured in the supremum norm, so one approximation works at every point. The degenerate interval $a=b$ needs only a constant function. A change of variable $t=(x-a)/(b-a)$ reduces the nondegenerate case to $[0,1]$.^[ref]

This is useful because a polynomial is described by finitely many coefficients and can be evaluated with addition and multiplication. Its surprise is that the target need not be differentiable: the sharp corner of an absolute value function can be approximated by smooth polynomials. The Bernstein approximation of a corner below makes this visible. Approximating values, however, does not by itself approximate derivatives; Weierstrass approximation does not preserve derivatives gives a counterexample.