Definition of the Derivative - Complete Interactive Lesson
Part 1: From Average to Instantaneous Rate of Change
∫ The Derivative as a Limit
Part 1 of 7 — From Average to Instantaneous Rate of Change
1. Average Rate of Change
The average rate of change of f on [a,b] is the slope of the secant line:
b−af(b)−f(a)
2. Instantaneous Rate of Change
As b→a, the secant line becomes the tangent line, and we get the derivative:
f′(a)=limh→0
This is the limit definition of the derivative — the most fundamental formula in calculus.
3. Computing Derivatives from the Definition
Example: Find f′(x) for f(x)=x2.
f′
4. Alternate Form
f′(a)=limx→a
This form is useful when given a specific point rather than a general formula.
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Two Forms of the Derivative Definition
Form
Formula
Use When
Standard
limh→0h
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Identify the Derivative 🔍
Match each limit to the derivative it represents.
Part 2: When Derivatives Exist (and When They Don't)
∫ Differentiability
Part 2 of 7 — When Derivatives Exist (and When They Don't)
1. Differentiability Requires Continuity
If f is differentiable at x=c, then f is continuous at x=.
Part 3: Reading Derivatives from Graphs
∫ Graphical Interpretation of Derivatives
Part 3 of 7 — Reading Derivatives from Graphs
1. Derivative = Slope of Tangent Line
At any point, f′(a) equals the slope of the tangent line to f at x=a.
Part 4: The Language of Derivatives
∫ Derivative Notation
Part 4 of 7 — The Language of Derivatives
1. Common Notations
All of these mean "the derivative of y with respect to x":
Notation
Read as
Emphasized by
f′(
Part 5: The Tangent Line Equation
∫ Tangent Lines and Linear Approximation
Part 5 of 7 — The Tangent Line Equation
1. Equation of the Tangent Line
The tangent line to f at x=a has:
Slope:m=f
Part 6: Derivative Definition Practice
∫ Problem-Solving Workshop
Part 6 of 7 — Derivative Definition Practice
Strategy: Limit-Definition Problems
When asked to find a derivative using the limit definition:
Write out hf(x+h)−f(x)
Expand carefully
Part 7: Comprehensive Review
∫ Review & Applications
Part 7 of 7 — Comprehensive Review
The Big Picture
The derivative f′(a) answers: "How fast is f changing at x=a?"
Geometrically: slope of the tangent line
h
f(a+h)−f(a)
(
x
)
=
limh→0h(x+h)2−x2=
limh→0hx2+2xh+h2−x2=
limh→0h2xh+h2=
limh→0(2x+
h)=
2x
x−a
f(x)−f(a)
f(x+h)−f(x)
Finding f′(x) as a function
Alternate
limx→ax−af(x)−f(a)
Finding f′(a) at a specific point
AP Exam note: You may be given a limit and asked to identify it as a derivative. For example, limh→0hsin(π/6+h)−1/2 represents f′(π/6) where f(x)=sinx.
c
Contrapositive: If f is NOT continuous at c, then f is NOT differentiable at c.
⚠️ The converse is false: f(x)=∣x∣ is continuous at x=0 but NOT differentiable there.
2. When Derivatives Fail to Exist
The derivative does not exist at:
Corners/cusps:f(x)=∣x∣ at x=0 (left slope =−1, right slope =1)
Vertical tangent lines:f(x)=x1/3 at x=0 (slope →±)
Discontinuities: Any type of discontinuity
Endpoints: Only one-sided derivative exists
3. Checking Differentiability for Piecewise Functions
Both derivatives equal 2, so f IS differentiable at x=1.
4. Local Linearity
A differentiable function "looks like a line" when you zoom in enough. This is the geometric meaning of differentiability — the graph has no sharp turns or breaks at the microscopic level.
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Quick Reference: Differentiability vs. Continuity
Function
Continuous at x=0?
Differentiable at x=0?
f(x)=x2
Yes
Yes
f(x)=∥x∥
Yes
No (corner)
f(x)=x1/3
Yes
No (vertical tangent)
f(x)=1/x
No
No
Memory aid: Differentiable ⟹ Continuous, but Continuous ⟹ Differentiable is FALSE.
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Differentiability Check 🔍
f′(a)>0: f is increasing at a
f′(a)<0: f is decreasing at a
f′(a)=0: f has a horizontal tangent at a (possible max, min, or inflection)
2. From Graph of f to Graph of f′
Feature of f
Corresponding feature of f′
f increasing
f′>0 (above x-axis)
f decreasing
f′<0 (below x-axis)
Local max of f
f′=0 (crosses from + to −)
Local min of f
f′=0 (crosses from − to +)
Inflection point of f
Local max or min of f′
f concave up
f′ increasing
f concave down
f′ decreasing
3. Estimating Derivatives from Data
From a table of values, approximate f′(a) using the symmetric difference quotient:
f′(a)≈2hf(a+h)−f(a−h)
This is more accurate than the one-sided difference quotient.
4. Reading f from f′
Given the graph of f′:
Where f′>0, f is increasing
Where f′<0, f is decreasing
Where f′ changes sign, f has a local extremum
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AP Exam Graph-Reading Tips
When given the graph of f′ and asked about f:
Zeros of f′ = horizontal tangent lines of f (possible extrema)
Sign changes of f′ = extrema of f
Extrema of f′ = inflection points of f
f′ positive = f rising, f′ negative = f falling
Common trap: A zero of f′ is NOT always an extremum. If f′ doesn't change sign (like f(x)= at ), it's just an inflection point.
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From f to f′ 🔍
Determine the sign of f′ at each point.
x
)
"f prime of x"
Lagrange
dxdy
"dydx"
Leibniz
dxd[f(x)]
"d dx of f(x)"
Operator form
y˙
"y dot"
Newton (time derivatives)
2. Leibniz Notation: More Than Just a Symbol
dxdy is NOT a fraction, but it behaves like one in many situations:
Chain rule:dxdy=dudy⋅dxdu (cancels like fractions!)
Evaluated at a point:dxdyx=3 means "evaluate the derivative at x=3"
3. Higher-Order Derivatives
Order
Lagrange
Leibniz
First
f′(x)
dxdy
Second
f′′(x)
dx2
Third
f′′′(x)
dx3
n-th
f(n)(x)
4. Units of Derivatives
If y has units of meters and x has units of seconds, then:
dxdy has units of secondsmeters (velocity)
dx2d2y has units of (acceleration)
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When to Use Which Notation
f′(x) — Best for general function manipulation, stating rules
dxdy — Best for related rates, implicit differentiation, chain rule
dxd[expression] — Best as an operator: "take the derivative of this expression"
Example in context: "Find dxd[x2sinx]" means "differentiate with respect to ."
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Match the Notation 🔍
′
(
a
)
Point:(a,f(a))
Point-slope form:y−f(a)=f′(a)(x−a)
2. Worked Example
Find the tangent line to f(x)=x3 at x=2.
f(2)=8, so the point is (2,8)
f′(x)=3x2, so f′(2)=12
Tangent line: y−8=12(x−2) → y=12x−16
3. Normal Line
The normal line is perpendicular to the tangent line. If the tangent slope is m, the normal slope is −m1 (negative reciprocal).
4. Tangent Line as a Local Approximation
Near x=a, the function f(x) is well-approximated by its tangent line:
f(x)≈f(a)+f′(a)(x−a)
This is called linearization or linear approximation.
Example: Approximate 4.1 using the tangent line to f(x)=x at x=4:
f(4)=2, f′(x)=2x1, f′(4)=41
4.1≈2+41(4.1−4)=2+0.025=2.025
(Actual: 4.1=2.02485...)
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Tangent Line Checklist
Find f(a) — the y-value at the point
Find f′(a) — the slope at the point
Write: y−f(a)=f′(a)(x−a)
Is the Approximation an Over- or Under-estimate?
If f is concave up near a: tangent line is below the curve → underestimate
If f is concave down near a: tangent line is above the curve → overestimate
This is a common AP FRQ follow-up question!
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Tangent Line Practice 🔍
f(x+h)
Simplify — everything should cancel the h in the denominator