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🎯⭐ INTERACTIVE LESSON

Linearization & Differentials

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Linearization & Differentials - Complete Interactive Lesson

Part 1: The Tangent Line Approximation

Linearization & Differentials

Part 1 of 7 — The Tangent Line Approximation

Topic Overview

PartTopic
1Tangent line approximation
2Approximating values
3Differentials
4Error analysis
5Applications & related rates
6AP-style workshop
7Comprehensive assessment

Local Linearization Formula

L(x)=f(a)+f′(a)(x−a)\boxed{L(x) = f(a) + f'(a)(x - a)}

ComponentMeaning
aaBase point (choose a "nice" value)
f(a)f(a)Known function value at aa
f′(a)f'(a)Slope of tangent line at aa
x−ax - aSmall displacement from aa

Worked Example

Approximate 4.1\sqrt{4.1} using linearization.

f(x)=xf(x) = \sqrt{x}, a=4a = 4 (nearest perfect square).

f(4)=2f(4) = 2, f′(x)=12xf'(x) = \frac{1}{2\sqrt{x}}, f′(4)=14f'(4) = \frac{1}{4}

L(x)=2+14(x−4)L(x) = 2 + \frac{1}{4}(x-4) L(4.1)=2+14(0.1)=2.025L(4.1) = 2 + \frac{1}{4}(0.1) = \boxed{2.025}

Actual: 4.1≈2.02485\sqrt{4.1} \approx 2.02485. Error ≈0.00015\approx 0.00015!

Key Fact: Choose aa to be a nearby value where f(a)f(a) and f′(a)f'(a) are easy to compute. The closer xx is to aa, the better the approximation.

Practice — Linearization 🎯

Build the linearization. 🔍

Approximate. ✍️

Key Takeaways — Part 1

  • Linearization: L(x)=f(a)+f′(a)(x−a)L(x) = f(a) + f'(a)(x-a)
  • Choose aa near xx where f(a)f(a) and f′(a)f'(a) are easy
  • The approximation improves as x→ax \to a
  • This is the tangent line at x=ax = a used as an approximation

Part 2: Differentials

Linearization & Differentials

Part 2 of 7 — Approximating Values

Common Linearizations at a=0a = 0

FunctionLinear Approximation near 00
sin⁡x\sin x≈x\approx x
cos⁡x\cos x≈1\approx 1
tan⁡x\tan x≈x\approx x
exe^x≈1+x\approx 1 + x
ln⁡(1+x)\ln(1+x)≈x\approx x
(1+x)n(1+x)^n≈1+nx\approx 1 + nx

Key Fact: These linearizations appear frequently on the AP exam, especially ex≈1+xe^x \approx 1+x and sin⁡x≈x\sin x \approx x for small xx.

Over/Under Estimates from Concavity

Concave up⇒Tangent line underestimates\boxed{\text{Concave up} \Rightarrow \text{Tangent line underestimates}} Concave down⇒Tangent line overestimates\boxed{\text{Concave down} \Rightarrow \text{Tangent line overestimates}}

Concavity at aaTangent line is a...
f′′(a)>0f''(a) > 0 (concave up)Underestimate
f′′(a)<0f''(a) < 0 (concave down)Overestimate

Worked Example

Approximate e0.1e^{0.1} using linearization. Is it an over- or underestimate?

f(x)=exf(x) = e^x, a=0a = 0. L(x)=1+xL(x) = 1 + x. L(0.1)=1.1L(0.1) = 1.1.

f′′(x)=ex>0f''(x) = e^x > 0 everywhere — concave up — so tangent line is an underestimate.

Actual: e0.1≈1.10517e^{0.1} \approx 1.10517. Indeed 1.1<1.105171.1 < 1.10517.

Practice — Approximations 🎯

Classify each approximation. 🔍

Calculate. ✍️

Key Takeaways — Part 2

  • Memorize common linearizations: sin⁡x≈x\sin x \approx x, ex≈1+xe^x \approx 1+x, ln⁡(1+x)≈x\ln(1+x) \approx x
  • Concave up (f′′>0f'' > 0) ⇒\Rightarrow underestimate
  • Concave down (f′′<0f'' < 0) ⇒\Rightarrow overestimate
  • AP frequently asks "is this an over- or underestimate?"

Part 3: Over/Underestimates

Linearization & Differentials

Part 3 of 7 — Differentials

Differentials vs. Derivatives

dy=f′(x) dx\boxed{dy = f'(x)\,dx}

ConceptNotationMeaning
Derivativedydx=f′(x)\frac{dy}{dx} = f'(x)Instantaneous rate of change
Differential of yydy=f′(x) dxdy = f'(x)\,dxApproximate change in yy
Actual changeΔy=f(x+Δx)−f(x)\Delta y = f(x+\Delta x) - f(x)Exact change in yy

Relationship

Δy≈dywhen dx=Δx is small\Delta y \approx dy \quad \text{when } dx = \Delta x \text{ is small}

The differential dydy is the change along the tangent line. The actual change Δy\Delta y is the change along the curve.

Worked Example

y=x3y = x^3. Find dydy when x=2x = 2 and dx=0.01dx = 0.01.

dy=f′(x) dx=3x2 dx=3(4)(0.01)=0.12dy = f'(x)\,dx = 3x^2\,dx = 3(4)(0.01) = 0.12

Actual change: Δy=(2.01)3−8=8.120601−8=0.120601\Delta y = (2.01)^3 - 8 = 8.120601 - 8 = 0.120601

dy=0.12dy = 0.12 vs Δy≈0.1206\Delta y \approx 0.1206 — very close!

Key Fact: dydy is a linear approximation to Δy\Delta y. The smaller dxdx, the better the approximation.

Practice — Differentials 🎯

Compare dydy and Δy\Delta y. 🔍

Compute the differential. ✍️

Key Takeaways — Part 3

  • dy=f′(x) dxdy = f'(x)\,dx is the differential (change along tangent)
  • Δy=f(x+dx)−f(x)\Delta y = f(x+dx) - f(x) is the actual change
  • dy≈Δydy \approx \Delta y for small dxdx
  • The derivative dy/dxdy/dx is the ratio of differentials

Part 4: Percentage Error

Linearization & Differentials

Part 4 of 7 — Error Analysis

Error Terminology

TermSymbolFormula
Approximate changedydyf′(x) dxf'(x)\,dx
Exact changeΔy\Delta yf(x+Δx)−f(x)f(x+\Delta x) - f(x)
Absolute error$\Delta y - dy
Relative error$\frac{\Delta y - dy
Percent error$\frac{\Delta y - dy

Over/Under with Error Bounds

Error≈12f′′(a)(x−a)2\boxed{\text{Error} \approx \frac{1}{2}f''(a)(x-a)^2}

This is the next term in the Taylor expansion. For small (x−a)(x-a), the error is approximately quadratic in the displacement.

Worked Example

Approximate 26\sqrt{26} using a=25a = 25. Estimate the error.

L(26)=5+110(1)=5.1L(26) = 5 + \frac{1}{10}(1) = 5.1

Error ≈12f′′(25)(1)2=12(−14⋅125)=−11000=−0.001\approx \frac{1}{2}f''(25)(1)^2 = \frac{1}{2}\left(-\frac{1}{4 \cdot 125}\right) = -\frac{1}{1000} = -0.001

So 26≈5.1−0.001=5.099\sqrt{26} \approx 5.1 - 0.001 = 5.099. Actual: 5.0990…5.0990\ldots — extremely close!

AP Tip: The sign of f′′(a)f''(a) tells you whether LL overestimates (f′′<0f'' < 0) or underestimates (f′′>0f'' > 0). The magnitude tells you how large the error is.

Practice — Error Analysis 🎯

Error classification. 🔍

Calculate the error. ✍️

Key Takeaways — Part 4

  • Error in linearization ∝(x−a)2\propto (x-a)^2 (quadratic)
  • Concave up ⇒\Rightarrow underestimate; concave down ⇒\Rightarrow overestimate
  • Absolute error =∣Δy−dy∣= |\Delta y - dy|; relative error =∣Δy−dy∣/∣Δy∣= |\Delta y - dy|/|\Delta y|
  • Stay close to aa for smaller errors

Part 5: Linearization with Tables

Linearization & Differentials

Part 5 of 7 — Applications

Propagation of Error

If a measurement xx has uncertainty dxdx, then the uncertainty in f(x)f(x) is approximately:

df=∣f′(x)∣ dx\boxed{df = |f'(x)|\,dx}

Worked Example: Sphere Volume

A sphere has radius r=5r = 5 cm with measurement error dr=0.02dr = 0.02 cm. Estimate the error in the volume.

V=43πr3V = \frac{4}{3}\pi r^3, V′(r)=4πr2V'(r) = 4\pi r^2

dV=4π(25)(0.02)=2π≈6.28 cm3dV = 4\pi(25)(0.02) = 2\pi \approx 6.28 \text{ cm}^3

Relative error: dVV=4πr2 dr43πr3=3 drr=3(0.02)5=0.012=1.2%\frac{dV}{V} = \frac{4\pi r^2\,dr}{\frac{4}{3}\pi r^3} = \frac{3\,dr}{r} = \frac{3(0.02)}{5} = 0.012 = 1.2\%

Key Fact: For V=43πr3V = \frac{4}{3}\pi r^3, the relative error in volume is 3 times the relative error in radius: dVV=3drr\frac{dV}{V} = 3\frac{dr}{r}.

Linearization from a Table

AP problems often give a table of ff and f′f' values and ask you to approximate ff at a nearby point:

xxf(x)f(x)f′(x)f'(x)
225533
441111−1-1

Approximate f(2.1)f(2.1): L(2.1)=f(2)+f′(2)(0.1)=5+3(0.1)=5.3L(2.1) = f(2) + f'(2)(0.1) = 5 + 3(0.1) = 5.3

Approximate f(3.9)f(3.9): L(3.9)=f(4)+f′(4)(−0.1)=11+(−1)(−0.1)=11.1L(3.9) = f(4) + f'(4)(-0.1) = 11 + (-1)(-0.1) = 11.1

Practice — Applications 🎯

AP table problem. 🔍

Propagation of error. ✍️

Key Takeaways — Part 5

  • Error propagation: df≈∣f′(x)∣ dxdf \approx |f'(x)|\,dx
  • Relative error: df/fdf/f. For V=crnV = cr^n: dV/V=n dr/rdV/V = n\,dr/r
  • Table-based linearization: L(x)=f(a)+f′(a)(x−a)L(x) = f(a) + f'(a)(x-a) using given values
  • AP loves "approximate f(2.1)f(2.1) given f(2)f(2) and f′(2)f'(2)" questions

Part 6: Problem-Solving Workshop

Linearization & Differentials

Part 6 of 7 — AP-Style Workshop

AP FRQ Patterns

PatternWhat They Ask
Table + tangent line"Use the tangent line at x=ax = a to approximate f(b)f(b)"
Over/under"Is your estimate an over- or underestimate? Justify."
Differential"What is dydy when x=ax = a and dx=hdx = h?"
Setup"Write the linearization of ff at x=ax = a"

Full Worked AP Problem

The table below gives values of a twice-differentiable function ff.

xx00112233
f(x)f(x)44773311
f′(x)f'(x)22−1-1−3-3−2-2

(a) Write the linearization of ff at x=2x = 2.

(b) Use your answer to approximate f(1.8)f(1.8).

(c) Given f′′(2)=−1f''(2) = -1, is your estimate an over- or underestimate? Justify.

Solution (a): L(x)=f(2)+f′(2)(x−2)=3+(−3)(x−2)=3−3(x−2)L(x) = f(2) + f'(2)(x-2) = 3 + (-3)(x-2) = 3 - 3(x-2)

Solution (b): L(1.8)=3−3(1.8−2)=3−3(−0.2)=3+0.6=3.6L(1.8) = 3 - 3(1.8-2) = 3 - 3(-0.2) = 3 + 0.6 = 3.6

Solution (c): Since f′′(2)=−1<0f''(2) = -1 < 0, ff is concave down near x=2x = 2. The tangent line lies above the curve, so 3.63.6 is an overestimate.

AP Tip: For "justify," you must state the concavity (f′′<0f'' < 0), explain what it means (concave down), and conclude (tangent above curve = overestimate).

AP-Style Practice 🎯

Justify your reasoning. 🔍

AP Problem. ✍️

Key Takeaways — Part 6

  • AP FRQs often combine table data with linearization
  • Always justify over/underestimate with concavity
  • Three-part justification: sign of f′′f'', concavity, conclusion
  • The tangent line approximation is exact when ff is linear

Part 7: Final Assessment

Linearization & Differentials

Part 7 of 7 — Comprehensive Assessment

Formula Reference

FormulaExpression
LinearizationL(x)=f(a)+f′(a)(x−a)L(x) = f(a) + f'(a)(x-a)
Differentialdy=f′(x) dxdy = f'(x)\,dx
Error estimate≈12f′′(a)(x−a)2\approx \frac{1}{2}f''(a)(x-a)^2
Concave up (f′′>0f'' > 0)Tangent underestimates
Concave down (f′′<0f'' < 0)Tangent overestimates

Common Linearizations at a=0a = 0

f(x)f(x)L(x)L(x)
sin⁡x\sin xxx
cos⁡x\cos x11
exe^x1+x1 + x
ln⁡(1+x)\ln(1+x)xx
(1+x)n(1+x)^n1+nx1 + nx

Common AP Mistakes

MistakeCorrect Approach
Choosing aa far from xxPick aa as close as possible
Forgetting f′(a)f'(a) in L(x)L(x)L(x)=f(a)+f′(a)(x−a)L(x) = f(a) + f'(a)(x-a), not f(a)+(x−a)f(a) + (x-a)
Incomplete justificationState f′′f'' sign, concavity, AND over/under
Confusing dydy and Δy\Delta ydydy = tangent line change; Δy\Delta y = actual change

Quiz Set 1 — Core Skills 🎯

Quiz Set 2 — Applications 🎯

Final review. 🔍

Final Challenge. ✍️

🎉 Topic Complete!

You've mastered Linearization & Differentials:

PartTopicStatus
1Tangent line approximation✅
2Approximating values✅
3Differentials✅
4Error analysis✅
5Applications✅
6AP-style workshop✅
7Comprehensive assessment✅

Key Fact: Linearization L(x)=f(a)+f′(a)(x−a)L(x) = f(a) + f'(a)(x-a) is the foundation for approximation in calculus. On the AP exam, always pair your estimate with "over/underestimate" justified by concavity.