Section 2.1 defined the derivative of a function and gave examples of how to compute it using its definition (i.e., using limits). The section also started with a brief motivation for this definition, that is, finding the instantaneous velocity of a falling object given its position function. Section 2.3 will give us more accessible tools for computing the derivative; tools that are easier to use than repeated use of limits.
This section falls in between the βWhat is the definition of the derivative?β and βHow do I compute the derivative?β sections. Here we are concerned with βWhat does the derivative mean?β, or perhaps, when read with the right emphasis, βWhat is the derivative?β We offer two interconnected interpretations of the derivative, hopefully explaining why we care about it and why it is worthy of study.
Section 2.1 started with an example of using the position of an object (in this case, a falling amusement-park rider) to find the objectβs velocity. This type of example is often used when introducing the derivative because we tend to readily recognize that velocity is the instantaneous rate of change in position. In general, if is a function of , then measures the instantaneous rate of change of with respect to . Put another way, the derivative answers βWhen changes, at what rate does change?β Thinking back to the amusement-park ride, we asked βWhen time changed, at what rate did the height change?β and found the answer to be βBy feet per second.β
Now imagine driving a car and looking at the speedometer, which reads β60 mph.β Five minutes later, you wonder how far you have traveled. Certainly, lots of things could have happened in those minutes; you could have intentionally sped up significantly, you might have come to a complete stop, you might have slowed to 20 mph as you passed through construction. But suppose that you know, as the driver, none of these things happened. You know you maintained a fairly consistent speed over those minutes. What is a good approximation of the distance traveled?
One could argue the only good approximation, given the information provided, would be based on βdistanceratetime.β In this case, we assume a constant rate of 60 mph with a time of minutes or of an hour. Hence we would approximate the distance traveled as miles.
Referring back to the falling amusement-park ride, knowing that at the velocity was ft/s, we could reasonably approximate that second later the ridersβ height would have dropped by about feet. Knowing that the riders were accelerating as they fell would inform us that this is an under-approximation. If all we knew was that and , weβd know that weβd have to stop the riders quickly otherwise they would hit the ground!
It is useful to recognize the units of the derivative function. If is a function of , i.e., for some function , and is measured in feet and in seconds, then the units of are βfeet per second,β commonly written as βft/s.β In general, if is measured in units and is measured in units , then will be measured in units β per β, or β.β Here we see the fraction-like behavior of the derivative in the notation: the units of are units of units of .
Let represent the world population minutes after 12:00 a.m., January 1, 2012. It is fairly accurate to say that (prb.orgβ1β). It is also fairly accurate to state that ; that is, at midnight on January 1, 2012, the population of the world was growing by about 156 people per minute (note the units). Twenty days later (or minutes later) we could reasonably assume the population grew by about people.
The term widget is an economic term for a generic unit of manufacturing output. Suppose a company produces widgets and knows that the market supports a price of per widget. Let give the profit, in dollars, earned by manufacturing and selling widgets. The company likely cannot make a (positive) profit making just one widget; the start-up costs will likely exceed . Mathematically, we would write this as .
The equation means that selling widgets returns a profit of . We interpret as meaning that when we are selling widgets, the profit is increasing at rate of per widget (the units are βdollars per widget.β) Since we have no other information to use, our best approximation for is:
widgets widget.
We approximate that selling widgets returns a profit of .
The previous examples made use of an important approximation tool that we first used in our previous βdriving a car at 60 mphβ example at the beginning of this section. Five minutes after looking at the speedometer, our best approximation for distance traveled assumed the rate of change was constant. In Examples 2.2.1 and Example 2.2.2 we made similar approximations. We were given rate of change information which we used to approximate total change. Notationally, we would say that
This approximation is best when is βsmall.β Small is a relative term; when dealing with the world population, days minutes is small in comparison to years. When manufacturing widgets, widgets is small when one plans to manufacture thousands.
One of the most fundamental applications of the derivative is the study of motion. Let be a position function, where is time and is distance. For instance, could measure the height of a projectile or the distance an object has traveled.
Letβs let measure the distance traveled, in feet, of an object after seconds of travel. Then has units βfeet per second,β and measures the instantaneous rate of distance change with repsect to time β it measures velocity.
Now consider , a velocity function. That is, at time , gives the velocity of an object. The derivative of ,, gives the instantaneous rate of velocity change with respect to time β acceleration. (We often think of acceleration in terms of cars: a car may βgo from to in seconds.β This is an average acceleration, a measurement of how quickly the velocity changed.) If velocity is measured in feet per second, and time is measured in seconds, then the units of acceleration (i.e., the units of ) are βfeet per second per second,β or ft/s/s. We often shorten this to βfeet per second squared,β or ftβs2, but this tends to obscure the meaning of the units.
A constant acceleration of ftss means that the velocity changes by fts each second. For instance, let measure the velocity of a ball thrown straight up into the air, where has units ft/s and is measured in seconds. The ball will have a positive velocity while traveling upwards and a negative velocity while falling down. The acceleration is thus fts. If fts, then second later, the velocity will have decreased by fts; that is, ft/s. We can continue: ft/s. Working backward, we can also figure that fts.
We now consider the second interpretation of the derivative given in this section. This interpretation is not independent from the first by any means; many of the same concepts will be stressed, just from a slightly different perspective.
Given a function , the difference quotient gives a change in values divided by a change in values; i.e., it is a measure of the βrise over run,β or βslope,β of the secant line that goes through two points on the graph of : and . As shrinks to , these two points come close together; in the limit we find , the slope of a special line called the tangent line that intersects only once near .
Lines have a constant rate of change, their slope. Nonlinear functions do not have a constant rate of change, but we can measure their instantaneous rate of change at a given value by computing . We can get an idea of how is behaving by looking at the slopes of its tangent lines. We explore this idea in the following example.
Consider as shown in Figure 2.2.5. It is clear that at the function is growing faster than at , as it is steeper at . How much faster is it growing at compared to ?
We can answer this exactly (and quickly) after Section 2.3, where we learn to quickly compute derivatives. For now, we will answer graphically, by considering the slopes of the respective tangent lines.
Figure2.2.6.A graph of and tangent lines at and .
With practice, one can fairly effectively sketch tangent lines to a curve at a particular point. In Figure 2.2.6, we have sketched the tangent lines to at and , along with a grid to help us measure the slopes of these lines. At , the slope is ; at , the slope is . Thus we can say not only is growing faster at than at , it is growing three times as fast.
To find the appropriate slopes of tangent lines to the graph of , we need to look at the corresponding values of .
The slope of the tangent line to at is ; this looks to be about .
The slope of the tangent line to at is ; this looks to be about .
The slope of the tangent line to at is ; this looks to be about .
Using these slopes, tangent line segments to are sketched in Figure 2.2.9. Included on the graph of in this figure are points where , and to help better visualize the value of at those points.
Figure 2.2.11 shows the graph of along with its tangent line, zoomed in at . Notice that near , the tangent line makes an excellent approximation of . Since lines are easy to deal with, often it works well to approximate a function with its tangent line. (This is especially true when you donβt actually know much about the function at hand, as we donβt in this example.)
While the tangent line to was drawn in Example 2.2.7, it was not explicitly computed. Recall that the tangent line to at is . While is not explicitly given, by the graph it looks like . Recalling that , we can compute the tangent line to be approximately . It is often useful to leave the tangent line in point-slope form.
To use the tangent line to approximate , we simply evaluate at instead of .
To demonstrate the accuracy of the tangent line approximation, we now state that in Example 2.2.10, . We can evaluate . Had we known all along, certainly we could have just made this computation. In reality, we often only know two things:
For instance, we can easily observe the location of an object and its instantaneous velocity at a particular point in time. We do not have a βfunction β for the location, just an observation. This is enough to create an approximating function for .
If we know and for some value , then computing the tangent line at is easy: . In Example 2.2.10, we used the tangent line to approximate a value of . Letβs use the tangent line at to approximate a value of near ; i.e., compute to approximate , assuming again that is βsmall.β Note:
This is the exact same approximation method used above! Not only does it make intuitive sense, as explained above, it makes analytical sense, as this approximation method is simply using a tangent line to approximate a functionβs value.
The importance of understanding the derivative cannot be understated. When is a function of , measures the instantaneous rate of change of with respect to and gives the slope of the tangent line to at .