Returning to the process of successive differentiation, it may be asked: Why does anybody want to differentiate twice over? We know that when the variable quantities are space and time, by differentiating twice over we get the acceleration of a moving body, and that in the geometrical interpretation, as applied to curves, \(\dfrac{dy}{dx}\) means the *slope* of the curve. But what can \(\dfrac{d^2 y}{dx^2}\) mean in this case? Clearly it means the rate (per unit of length \(x\)) at which the slope is changing—in brief, it is *a measure of the curvature of the slope*.

Suppose a slope constant, as in Fig. 31.

Here, \(\dfrac{dy}{dx}\) is of constant value.

Suppose, however, a case in which, like Fig. 32, the slope itself is getting greater upwards, then \(\dfrac{d\left(\dfrac{dy}{dx}\right)}{dx}\), that is, \(\dfrac{d^2y}{dx^2}\), will be *positive*.

If the slope is becoming less as you go to the right (as in Fig. 14, chapter 10), or as in Fig. 33, then, even though the curve may be going upward, since the change is such as to diminish its slope, its \(\dfrac{d^2y}{dx^2}\) will be *negative*.

It is now time to initiate you into another secret—how to tell whether the result that you get by “equating to zero” is a maximum or a minimum. The trick is this: After you have differentiated (so as to get the expression which you equate to zero), you then differentiate a second time, and look whether the result of the second differentiation is *positive* or *negative*. If \(\dfrac{d^2y}{dx^2}\) comes out *positive*, then you know that the value of \(y\) which you got was a *minimum*; but if \(\dfrac{d^2y}{dx^2}\) comes out *negative*, then the value of \(y\) which you got must be a *maximum*. That’s the rule.

The reason of it ought to be quite evident. Think of any curve that has a minimum point in it (like Fig.15, chapter 10), or like Fig. 34, where the point of minimum \(y\) is marked \(M\), and the curve is *concave* upwards. To the left of \(M\) the slope is downward, that is, negative, and is getting less negative. To the right of \(M\) the slope has become upward, and is getting more and more upward. Clearly the change of slope as the curve passes through \(M\) is such that \(\dfrac{d^2y}{dx^2}\) is *positive*, for its operation, as \(x\) increases toward the right, is to convert a downward slope into an upward one.

Similarly, consider any curve that has a maximum point in it (like Fig. 16, chapter 10), or like Fig. 35, where the curve is *convex*, and the maximum point is marked \(M\). In this case, as the curve passes through \(M\) from left to right, its upward slope is converted into a downward or negative slope, so that in this case the “slope of the slope” \(\dfrac{d^2y}{dx^2}\) is *negative*.

Go back now to the examples of the last chapter and verify in this way the conclusions arrived at as to whether in any particular case there is a maximum or a minimum. You will find below a few worked out examples.

*Examples.*