Precalculus with Integrated CalculusXYZ Homework Edition

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6.3 The Shape of Graphs

We know if f is differentiable at x = a then the graph of f is locally linear at x = a and f ( a ) is the slope of the tangent line at the point ( a , f ( a ) ) . In this section, we explore how local behavior near a point can be extrapolated to global behavior over an interval. First, we review Definition 1.7 from Section 1.2:

Suppose a function satisfies f ( x ) > 0 for all x in an open interval1 I . Then we know that not only is the graph of f locally linear on I , but the slopes of all of the tangent lines are positive. This means that all of the tangent lines are increasing so it stands to reason that the function f is likewise increasing on I . In other words, if a function is locally increasing on I , then it is globally increasing on I as well.

We can apply the same reasoning above to situations where f ( x ) < 0 for all x in I , which implies f is decreasing on I or f ( x ) = 0 on I , which implies f is constant on I . In Calculus, you'll learn this fact is a consequence of the Mean Value Theorem.2 In this text, we'll just accept the following theorem is true and hope we've done enough hand-waving to deem it reasonable.

Theorem 6.4 may be visualized as follows:

We can use Theorem 6.4 to help us determine the (open) intervals over which a function f is increasing, decreasing, and constant by making a sign diagram for the derivative f .

In order to avoid us having to go through the (somewhat lengthy) process of finding f ( x ) using Definition 6.8, we'll just use some properties of derivatives from Calculus behind the scenes and present you with both a function and its derivative. It's time for an example.

We generalize our observations about local extrema in the following result.

Concavity and the Second Derivative

In section Section 4.2, we introduced the notion of concavity. In that section, we described curves as being concave up over an interval if it resembles a portion of a ` ' shape and concave down over an interval if resembles part of a ` ' shape. Now that we've had some exposure to Calculus, we can more precisely define these notions.

If we take the time to study a generic concave up curve, the ` ' shape can be divided into a decreasing and increasing arc:

Image: DecCU
Figure 6.61
Image: IncCU
Figure 6.62

slopes are increasing towards 0

slopes are increasing away from 0

In both of these cases, the slopes of the tangent line are increasing.

Likewise, we can dissect a generic ` ' shape curve into an increasing and decreasing arc:

Image: IncCD
Figure 6.63
Image: DecCD
Figure 6.64

slopes are decreasing towards 0

slopes are decreasing away from 0

Here, the slopes of the tangent line are decreasing.

We know from Theorem 6.4 that the derivative of a function can tell us where that function is increasing and decreasing. Since the function which gives us the slopes of tangent lines is the derivative, f ( x ) , we could use the derivative of f ( x ) to determine where the slopes of the tangent lines were increasing and decreasing. This leads us to define the second derivative, f ′′ ( x ) as the derivative of f ( x ) .

We present the following theorem without proof, but hopefully sufficiently motivated.

Note that we can use concavity to help us distinguish local extrema.

For the function above, both f ( 1 ) = 0 and f ( 3 ) = 0 . Note that f ′′ ( 1 ) < 0 which means f is concave down there. This forces f to have a local maximum at ( 1 , 10 ) . Likewise, f ′′ ( 3 ) > 0 which means f is concave up there. This forces f to have a local minimum at ( 3 , 22 ) . We generalize this observation below.

Our last example offers a twist on these sorts of curve-sketching problems.

Exercises

In Exercises -, use the given function f and its (first) derivative f to help you find:

  • the open intervals over which f is increasing, decreasing, and constant.
  • the local extrema.

Check your answers using a graphing utility.

  1. f ( x ) = 2 x 3 3 x 2 12 x + 1 , f ( x ) = 6 x 2 6 x 12
  2. f ( x ) = 10 x x 2 + 1 , f ( x ) = 10 10 x 2 ( x 2 + 1 ) 2
  3. f ( x ) = x x 2 3 , f ( x ) = 4 x 6 3 ( x 2 ) 2 3

In Exercises -, use the given function f and its second derivative f ′′ to help you find:

  • the open intervals over which the graph of f is concave up and concave down.
  • the inflection points in the graph.

Check your answers using a graphing utility.

  1. f ( x ) = 2 x 3 3 x 2 12 x + 1 , f ′′ ( x ) = 12 x 6
  2. f ( x ) = 10 x x 2 + 1 , f ′′ ( x ) = 20 x 3 60 x ( x 2 + 1 ) 3
  3. f ( x ) = x x 2 3 , f ′′ ( x ) = 4 ( x 3 ) 9 ( x 2 ) 5 3
  4. If a 0 , we showed in Exercise in Section 6.2 that if f ( x ) = a x 2 + b x + c , then f ( x ) = 2 a x + b . Solving f ( x ) = 0 produced x = b 2 a , the x -coordinate of the vertex of the parabola y = f ( x ) . This Exercise shows this is part of a pattern.

    1. If a 0 , show the x -coordinate of the x -intercept of the graph of y = a x + b is x = b a = b 1 a .
    2. If a 0 , for f ( x ) = a x 3 + b x 2 + c x + d it turns out that f ′′ ( x ) = 6 a x + 2 b . Show x = b 3 a is the x -coordinate of the inflection point of the graph of y = f ( x ) .
  5. In Exercise in Section 5.2, we observed that average cost appeared to be minimized when average cost was approximately equal to marginal cost. In this Exercise, we use Calculus and the tools from this section to show this.

    Recall if C ( x ) is the cost to produce x items, the average cost is defined as C ¯ ( x ) = C ( x ) x , x > 0 , is the cost per item.

    1. It turns out that C ¯ ( x ) = x C ( x ) C ( x ) x 2 . Show C ¯ ( x ) = 0 when C ( x ) = C ¯ ( x ) .
    2. It turns out that C ¯ ′′ ( x ) = x 2 C ′′ ( x ) 2 x C ( x ) + 2 C ( x ) x 3 .

      Show we can rewrite this as: C ¯ ′′ ( x ) = x C ′′ ( x ) 2 C ( x ) + 2 C ¯ ( x ) x 2 .

    3. Show that when C ( x ) = C ¯ ( x ) , then C ¯ ′′ ( x ) = C ′′ ( x ) x .
    4. It is usually assumed in most economic settings that for cost functions,7 C ′′ ( x ) > 0 . Use this and your results from parts and to prove that a minimum is produced when C ( x ) = C ¯ ( x ) .

      NOTE: In Exercise in Section 6.2, we saw how C ( x ) can be used to approximate the marginal cost, M C ( x ) , so we have established that in order to minimize average cost, we should look where the average cost matches the marginal cost.

  6. The complete graph of y = f ( x ) is shown below. Use the graph to answer the following questions.

    NOTE: Assume ( 2.029 , 1.82 ) is a local maximum and that ( 1.077 , 0.948 ) is an inflection point.

    Image: T04Graph
    Figure 6.75
    1. Determine the x -values where:

      f ( x ) = 0 :

      f ( x ) = 0 :

    2. List the open intervals over which:

      f ( x ) > 0 :

      f ( x ) < 0 :

      f ( x ) > 0 :

      f ( x ) < 0 :

      f ′′ ( x ) > 0 :

      f ′′ ( x ) < 0 :

  7. Below is the graph of y = f ( x ) for a continuous function f .

    Using Example 6.3.5 as a guide, sketch a probable graph of y = f ( x ) .

    Image: graphfromderivativeexercise
    Figure 6.76
  8. The graph below was taken from https://ohiohospitals.org/covid19data on January 28th, 2021:

    Image: COVIDPatients
    Figure 6.77

    With help from your classmate, highlight and label one segment on the graph which (roughly) represents the following scenarios.

    For brevity, we'll use `patients' to mean `inpatient COVID positive patients' and `the rate of change' to mean `the rate of change of inpatient COVID positive patients with respect to time.'

    1. The number of patients is decreasing and the rate of change is decreasing. (Label this `a.')
    2. The number of patients is decreasing and the rate of change is increasing. (Label this `b.')
    3. The number of patients is increasing and the rate of change is increasing. (Label this `c.')
    4. The number of patients is increasing and the rate of change is decreasing. (Label this `d.')
    5. Discuss with your classmates what the phrase `flatten the curve' could mean in terms of first and second derivatives. (See below for an illustration.)

      Image: flattenthecurve
      Figure 6.78

Answers

  1. increasing: ( , 1 ) , ( 2 , ) ; decreasing: ( 1 , 2 ) ; local max: ( 1 , 8 ) ; local min: ( 2 , 19 ) .
  2. increasing: ( 1 , 1 ) ; decreasing: ( , 1 ) , ( 1 , ) ; local max: ( 1 , 5 ) ; local min: ( 1 , 5 ) .
  3. increasing: ( 3 2 , ) ; decreasing: ( , 3 2 ) ; local (absolute) min: ( 3 2 , 3 2 2 3 )
  4. concave up: ( 1 2 , ) ; concave down: ( , 1 2 ) ; inflection point: ( 1 2 , 11 2 )
  5. concave up: ( 3 , 0 ) , ( 3 , ) ; concave down: ( , 3 ) , ( 0 , 3 ) ; inflection points: ( 3 , 5 3 2 ) , ( 0 , 0 ) , ( 3 , 5 3 2 )
  6. concave up: ( , 2 ) , ( 3 , ) ; concave down: ( 2 , 3 ) ; inflection points: ( 2 , 0 ) , ( 3 , 3 ) .
    1. To find the x -intercept, we set a x + b = 0 and get x = b a provided a 0 .
    2. Solving f ′′ ( x ) = 6 a x + 2 b = 0 , we get x = 2 b 6 a = b 3 a , provided a 0 .

      The graph of f ′′ ( x ) = 6 a x + 2 b is a line so we know on one side of x = b 3 a , f ′′ ( x ) > 0 and on the other side, f ′′ ( x ) < 0 .

      Hence, the graph of the original function y = f ( x ) changes concavity at x = b 3 a .

    1. To solve C ¯ ( x ) = x C ( x ) C ( x ) x 2 = 0 , we set the numerator, x C ( x ) C ( x ) = 0 . We get x C ( x ) = C ( x ) so C ( x ) = C ( x ) x = C ¯ ( x ) .
    2. Divide both numerator and denominator of C ¯ ′′ ( x ) = x 2 C ′′ ( x ) 2 x C ( x ) + 2 C ( x ) x 3 by x :

      C ¯ ′′ ( x ) = x C ′′ ( x ) 2 C ( x ) + 2 C ( x ) x x 2 and substitute C ( x ) x = C ¯ ( x ) .

    3. If C ( x ) = C ¯ ( x ) , then:

      C ¯ ′′ ( x ) = x C ′′ ( x ) 2 C ( x ) + 2 C ¯ ( x ) x 2 = x C ′′ ( x ) 2 C ¯ ( x ) + 2 C ¯ ( x ) x 2 = x C ′′ ( x ) x 2 = C ′′ ( x ) x .

    4. When C ( x ) = C ¯ ( x ) , we have that C ¯ ( x ) = 0 and C ¯ ′′ ( x ) > 0 . By the Second Derivative Test for Local Extrema, Theorem 6.7, the average cost C ¯ ( x ) has a minimum when C ( x ) = C ¯ ( x )
    1. f ( x ) = 0 : x = 0 , π

      f ( x ) = 0 : x = 2.029 ( f + ( 0 ) = 0 ., too.)

    2. f ( x ) > 0 : ( 0 , π )

      f ( x ) < 0 : none

      f ( x ) > 0 : ( 0 , 2.029 )

      f ( x ) < 0 : ( 2.029 , π )

      f ′′ ( x ) > 0 : ( 0 , 1.077 )

      f ′′ ( x ) < 0 : ( 1.077 , π ) .

  7. Answers vary. below is a sketch of the key features that should be included:

    Image: graphfromderivativeexerciseanswer
    Figure 6.79

Adapted from Precalculus, Preliminary 4th Edition (integrated calculus), by Carl Stitz and Jeff Zeager (stitz-zeager.com), licensed under CC BY-NC-SA 3.0. Changes were made: reformatted as an accessible XYZ web edition. License: CC-BY-NC-SA-3.0.

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