Adjust the parameters in the graph above to explore the relationships between variables.
F′(a)={limh→0}f{(a+h)−f(a)}{h}
Equivalently, using an alternate form:
F′(a)={limx→a}f{(x)−f(a)}{x−a}
If this limit exists, f is said to be differentiable at a.
Physical intuition. If s(t) is position at time t Then s′(t) is the instantaneous Velocity. The derivative answers: “how fast is f changing right now?”
Geometric intuition. The derivative f′(a) equals the slope of the tangent line to the graph of f at the point (a,f(a)).
Interpretations of the Derivative
Geometric:f′(a) is the slope of the tangent line to y=f(x) at the point (a,f(a)).
Physical: If s(t) is position, then s′(t) is instantaneous velocity.
Rate of change:f′(a) gives the instantaneous rate of change of f with respect to x at x=a.
:::note Example Find f′(2) for f(x)=x2−3x+1 using the limit definition.
Derivation of arcsinx. Let y=arcsinx So x=siny. Differentiating: 1=cosy⋅d{y}{dx}, so d{y}{dx}=1{}{cosy}=1{}{{1−sin2y}}=1{}{{1−x2}}.
Derivation of arctanx. Let y=arctanx So x=tany. Differentiating: 1=sec2y⋅dxdy So dxdy=sec2y1=cos2y.
Since sec2y=1+tan2y=1+x2We get cos2y=1+x21 So dxdy=1+x21. ■
Implicit Differentiation
When y is defined implicitly as a function of xDifferentiate both sides with respect to x And solve for dxdy. ::: :::note Example Find dxdy for x2+y2=25.
Differentiate both sides with respect to x:
2x+2yd{y}{dx}=0⟹d{y}{dx}=−x{}{y}
::: :::note Example Find the equation of the tangent line to x2+y2=25 at the point (3,4).
From the previous example, dxdy=−yx. At (3,4):
d{y}{dx}{(3,4)}=−3{}{4}
The tangent line is y−4=−43(x−3)Or 3x+4y=25. ::: :::note Example Find dxdy for x3+y3=6xy.
\dot{x}$$\ddot{x} (Newton’s dot notation for time derivatives)
Applications of Derivatives (CED Unit 5)
Related Rates
When two or more quantities are related by an equation, their rates of change are also related. ::: :::note Example A ladder 10 ft long rests against a vertical wall. If the bottom slides away at 1 ft/s, how fast is The top sliding down when the bottom is 6 ft from the wall?
Let x = distance from wall to bottom, y = height of top on wall. Then x2+y2=100.
The negative sign confirms the top is sliding down. ::: :::note Example A spherical balloon is inflated at a rate of 10 \mathrm{ cm^3/\mathrm{s. How fast is the radius Increasing when the radius is 5 cm? (V=34πr3)
::: :::note Example A 15 ft ladder leans against a wall. The bottom slides away at 2 ft/s. How fast is the angle between The ladder and the ground changing when the bottom is 9 ft from the wall?
Let θ be the angle between the ladder and the ground. Then cosθ=15x So x=15cosθ.
(The actual value is approximately 1.21899, showing that linear approximation is less accurate when x is farther from a.)
Mean Value Theorem (MVT)
If f is continuous on [a,b] and differentiable on (a,b) Then there exists at least one c∈(a,b) such that:
F′(c)=f{(b)−f(a)}{b−a}
This means the instantaneous rate of change equals the average rate of change at some interior Point.
Geometric interpretation. The MVT guarantees the existence of a tangent line parallel to the Secant line joining (a,f(a)) and (b,f(b)).
Proof of the MVT. Let g(x)=f(x)−b−af(b)−f(a)(x−a)−f(a). Then g(a)=g(b)=0. By Rolle’s Theorem, there exists c∈(a,b) with g′(c)=0Which gives f′(c)=b−af(b)−f(a). ■
Rolle’s Theorem
If f is continuous on [a,b]Differentiable on (a,b) And f(a)=f(b) Then there exists At least one c∈(a,b) such that f′(c)=0.
Rolle’s Theorem is a special case of the MVT where f(a)=f(b).
Application of Rolle’s Theorem. Rolle’s Theorem is used to prove that a differentiable function Has at most one root in an interval. If it had two roots, Rolle’s Theorem would give a point where The derivative is zero, leading to a contradiction. ::: :::note Example Show that f(x)=x3−3x+1 has exactly one real root.
Note that f(0)=1 and f(−2)=−8+6+1=−1. By the IVT, there is at least one root in (−2,0).
Suppose there are two distinct roots a<b. Then f(a)=f(b)=0 And by Rolle’s Theorem, There exists c∈(a,b) with f′(c)=0. But f′(x)=3x2−3=3(x+1)(x−1)Which is zero Only at x=±1. This does not lead to a contradiction on its own, so we must examine further.
In fact, f(-2) = -1$$f(0) = 1$$f(1) = -1$$f(2) = 3. So f has at least one root in (−2,0)At least one in (0,1) And possibly one in (1,2). Rolle’s Theorem applied to Consecutive roots shows that each pair of roots must bracket either x=−1 or x=1.
Testing: f(−1)=−1+3+1=3>0. So the root in (−2,0) is in (−2,−1) And the root in (0,1) is in (0,1). These bracket different critical points. In fact, f has three real roots, And Rolle’s Theorem confirms this is consistent since the critical points at x=±1 bracket the Three roots.
Curve Sketching and Analysis
Critical Points
A critical number of f is a value c in the domain of f where f′(c)=0 or f′(c) does Not exist.
First Derivative Test
Let c be a critical number:
If f′ changes from positive to negative at c Then f(c) is a local maximum.
If f′ changes from negative to positive at c Then f(c) is a local minimum.
If f′ does not change sign at c Then f(c) is neither.
Second Derivative Test
Let f′′(c) exist with f′(c)=0:
If f′′(c)>0 Then f(c) is a local minimum.
If f′′(c)<0 Then f(c) is a local maximum.
If f′′(c)=0The test is inconclusive.
When to use which test. The first derivative test always works. The second derivative test is Faster but sometimes inconclusive. When f′′(c)=0You must fall back to the first derivative Test.
Example where the second derivative test is inconclusive. Consider f(x)=x4. f′(x)=4x3=0 gives x=0 And f′′(x)=12x2 So f′′(0)=0 — the test is inconclusive. But f′(x)=4x3 changes from negative to positive at x=0 So f has a local minimum at x=0 by the first derivative test.
Optimization
To solve optimization problems:
Identify the quantity to optimize and the constraint.
Write an equation relating the variables.
Express the quantity as a function of a single variable.
Find critical points and apply the first or second derivative test.
Verify the result answers the original question. ::: :::note Example A farmer has 200 ft of fencing. Find the dimensions of the rectangular pen that maximizes the area.
Let x and y be the dimensions. Then 2x+2y=200 So y=100−x.
The minimum value is 4 at x=2. ::: :::note Example A rectangular box with a square base has volume 500 \mathrm{ cm^3. The material for the base costs Twice as much as the material for the sides. Minimise the cost of the material.
Let the base have side length x cm and height h cm. Let the cost per unit area of the sides be c. Then the base costs 2c per unit area.
The cost is minimised when the box is a cube. dx2d2C=4c+x34000c>0 Confirming a minimum.
Optimization on Closed Intervals
When the domain is a closed interval [a,b]The absolute maximum and minimum occur at critical Points or at endpoints.
Procedure:
Find all critical points in (a,b).
Evaluate f at all critical points and at both endpoints.
The largest value is the absolute max; the smallest is the absolute min. ::: :::note Example Find the absolute maximum and minimum of f(x)=x3−3x2+4 on [−1,4].
Absolute minimum: 0 at x=−1 and x=2. Absolute maximum: 20 at x=4. :::
Common Pitfalls
Forgetting the chain rule. When differentiating a composition, always account for the inner function’s derivative. For example, dxd[sin(x2)]=cos(x2).
Incorrect sign in the quotient rule. Remember: “Low d-High minus High d-Low, draw a line and square below.”
Confusing dxd[ex] with dxd[xe]. The former is ex; the latter is exe−1.
Implicit differentiation: forgetting to apply the chain rule to y terms. When differentiating y2 with respect to xThe result is 2ydxdyNot 2y.
Units in related rates. Always include units and check that they make dimensional sense.
Not checking endpoints in optimization problems on closed intervals.
Using the second derivative test when it is inconclusive (f′′(c)=0). Fall back to the first derivative test.
Confusing the second derivative with the first. The second derivative tells you about concavity, not the slope.
Dropping negative signs in implicit differentiation. Be systematic: write every y term with dxdy attached.
Linear approximation overreach. The approximation f(x)≈f(a)+f′(a)(x−a) is accurate only near x=a. Using it far from a can give wildly inaccurate results.
Practice Questions
Find dxdy for exy+y=x2 using implicit differentiation.
A spherical balloon is inflated at a rate of 10 \mathrm{ cm^3/\mathrm{s. How fast is the radius increasing when the radius is 5 cm? (V=34πr3)
Find all critical points of f(x)=x4−4x3+6x2−4x+1 and classify them using the second derivative test.
Use the Mean Value Theorem to show that ∣sinb−sina∣≤∣b−a∣ for all a,b.
Find the equation of the tangent line to y=ln(x2+1) at x=1.
A 15 ft ladder leans against a wall. The bottom slides away at 2 ft/s. How fast is the angle between the ladder and the ground changing when the bottom is 9 ft from the wall?
Find the absolute maximum and minimum of f(x)=x3−3x2+4 on [−1,4].
Use linear approximation to estimate (1.02)10.
Find dxd[e2xx2+1] and find all stationary points.
Find dx2d2y for x3+y3=6xy at the point (3,3).
A rectangular box with a square base has volume 500 \mathrm{ cm^3. The material for the base costs twice as much as the material for the sides. Minimise the cost of the material.
Prove that the derivative of f(x)=x3 is f′(x)=3x2 from first principles.
Find dxdy for sin(x+y)=xcosy.
Find the inflection points of f(x)=x4−6x2+4.
A conical tank (vertex pointing down) has radius 5 ft at the top and height 10 ft. Water is pumped in at 3 \mathrm{ft^3/\mathrm{min. How fast is the water level rising when the water is 6 ft deep?
Find the dimensions of the rectangle of maximum area that can be inscribed in a semicircle of radius r.
Practice Problems
Question 1: Implicit differentiation
Find dxdy for the curve x3+y3=6xy. Then find the equation of the tangent line at the point (3,3).
Verify that the function f(x)=x3−3x+1 satisfies the conditions of the Mean Value Theorem on [−2,2] And find all values of c that satisfy the conclusion.
Answer
f is a polynomial, so it is continuous on [−2,2] and differentiable on (−2,2).
Both values are in (−2,2). The MVT is satisfied at c=2/3 and c=−2/3.
:::tip Diagnostic Test Ready to test your understanding of Derivatives? The contains the hardest
questions within the AP specification for this topic, each with a full worked solution.
Unit tests probe edge cases and common misconceptions. Integration tests combine Derivatives with other AP Calculus topics to test synthesis under exam conditions.
See for instructions on self-marking and building a personal test matrix.
flowchart TD
A[2_Derivativesx] --> B[Key Concepts]
A --> C[Core Principles]
A --> D[Practical Applications]
B --> E[Fundamental definitions]
C --> F[Design patterns]
D --> G[Real-world usage]
Summary
This topic covers the mathematical techniques and concepts related to derivatives, including key theorems, methods, and problem-solving approaches.
Key concepts include:
sine, cosine, and tangent functions
trigonometric identities
solving trigonometric equations
the sine and cosine rules
radian measure and arc length
Regular practice with a variety of question types is essential to build fluency and confidence in applying these mathematical techniques.
Worked Examples
Worked examples demonstrating the application of key concepts are covered in the detailed sub-pages linked above. :::
Intuition
The speedometer analogy: A derivative tells you how fast something is changing at any instant, just like a speedometer shows your current speed without needing to measure average speed over a distance.
Why it matters: Derivatives are everywhere — from finding the optimal price for a product to understanding how diseases spread. They allow us to model and predict change in any system.
The key insight: A derivative captures the instantaneous rate of change by shrinking the measurement interval to zero.
Cross-References
Limits and Continuity — The derivative is defined as a limit, making limits the foundational concept for all differentiation.
Integrals — Integration is the reverse process of differentiation, connecting the two fundamental operations of calculus.
Differential Equations — Derivatives appear in differential equations, linking rate-of-change analysis to modelling real-world phenomena.
AP Physics — Kinematics: Velocity is the derivative of position, and acceleration is the derivative of velocity — derivatives model all rates of change in mechanics.
AP Physics — Newton’s Laws: Force equals mass times acceleration, where acceleration is a second derivative of position, connecting differentiation directly to dynamics.