Calculus I · Unit 2B · lesson

Differentials

Concept

Learning objectives

Use dy=f(x)dxdy=f'(x)dx to estimate changes in an output.

Differentials and Small Changes

Explanation

Before the formulas

The approximation in Differentials replaces a nonlinear function with its best local line. The known value supplies the starting height, and the derivative supplies the rate of change. The estimate is useful because lines are easy to calculate with, not because the original curve has become exactly linear.

Always identify the center aa and the target xx. Write Δx=xa\Delta x=x-a, calculate the predicted change f(a)Δxf'(a)\Delta x, and then add it to f(a)f(a). Use concavity or a numerical comparison to understand the direction and size of the error.

The curve produces the actual output change y. The tangent line produces the differential dy=f'(x)dx. Their vertical gap is the approximation error.
Read this graph as text

Differentials separate the actual change from the tangent-line estimate. The curve produces the actual output change y . The tangent line produces the differential dy=f'(x)dx . Their vertical gap is the approximation error. Starting at x=1 , the input changes by dx=0.7 . The tangent predicts dy=2(0.7)=1.4 , while the curve actually changes by y=1.89 . The two quantities are close only when the input change is sufficiently small; they are not identical by definition.

Every relationship in differentials separate the actual change from the tangent-line estimate is identified with written labels plus distinct solid, dashed, dotted, double, marker, or pattern cues; color is never the only carrier of meaning.

Why it matters: This visual prevents the common equation dy= y from being treated as exact. It should reinforce that dy belongs to the tangent model and y belongs to the original function.

Visual study

The curve produces the actual output change y. The tangent line produces the differential dy=f'(x)dx. Their vertical gap is the approximation error.

Explanation

Differentials package the tangent-line prediction

For an input change dxdx, the differential dy=f(x)dxdy=f'(x)dx is the change predicted by the tangent line. The actual change is Δy=f(x+dx)f(x)\Delta y=f(x+dx)-f(x). Near the base point, dydy and Δy\Delta y are close but not generally equal.

This notation is especially useful in measurement problems because it keeps the estimated output error attached to the input uncertainty and local sensitivity.

Differentials package the linear approximation into the compact relation

dy=f(x)dx.dy=f'(x)\,dx.

Here dxdx represents a chosen small input change, and dydy represents the corresponding linearized output change. The actual change Δy\Delta y is usually close to, but not exactly equal to, dydy.

This notation is especially useful when several measurements contribute to an engineering calculation. It preserves units and makes first-order error propagation visible.

If y=f(x)y=f(x), define the input differential dxdx as a chosen small change in xx. The corresponding differential in yy is

dy=f(x)dx.\boxed{dy=f'(x)\,dx}.

The actual output change is

Δy=f(x+Δx)f(x).\Delta y=f(x+\Delta x)-f(x).

For small Δx\Delta x,

Δydy.\Delta y\approx dy.
Guided walkthrough

Estimate a change in area

A circular plate has radius 1010 cm. Estimate the change in area if the radius increases by 0.030.03 cm.

Answer reveal

Worked solution

Write a real attempt before opening the supplied answer.

Concept

Derivative as a local conversion factor

The relation dy=f(x)dxdy=f'(x)dx says that near a chosen input, the derivative converts a small input change into an approximate output change.

Modeling lab

Volume added by a thin coating

A metal sphere of radius r=10r=10 cm receives a coating of thickness dr=0.02dr=0.02 cm. Since

V=43πr3,dV=4πr2dr,V=\frac43\pi r^3, \qquad dV=4\pi r^2\,dr,

we estimate

dV=4π(10)2(0.02)=8π cm3.dV=4\pi(10)^2(0.02)=8\pi\text{ cm}^3.

This is the surface area times thickness, exactly the geometric shell interpretation expected for a very thin coating.

After the explanation

Use the section idea

Reading lens

A differentiable curve behaves like its tangent line over a small enough neighborhood, with concavity explaining the direction of error.

Mental model

Linearization, differentials, and Newton's method reuse one local line for estimation, uncertainty, or a better root guess.

Decision

Choose a nearby easy input, record the local slope, and state why the requested change is small enough for the model.

Common trap

Presenting a tangent estimate as exact or running Newton iterations without checking residuals and failure modes.

Check yourself

Can you give the estimate, error direction, units, and a reason the local line is trustworthy here?

Source & rights

Original instruction with traceable references.

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