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Find the local linear approximation to f ( x ) = x 3 at x = 8 . Use it to approximate 8.1 3 to five decimal places.

L ( x ) = 2 + 1 12 ( x 8 ) ; 2.00833

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Linear approximation of sin x

Find the linear approximation of f ( x ) = sin x at x = π 3 and use it to approximate sin ( 62 ° ) .

First we note that since π 3 rad is equivalent to 60 ° , using the linear approximation at x = π / 3 seems reasonable. The linear approximation is given by

L ( x ) = f ( π 3 ) + f ( π 3 ) ( x π 3 ) .

We see that

f ( x ) = sin x f ( π 3 ) = sin ( π 3 ) = 3 2 f ( x ) = cos x f ( π 3 ) = cos ( π 3 ) = 1 2

Therefore, the linear approximation of f at x = π / 3 is given by [link] .

L ( x ) = 3 2 + 1 2 ( x π 3 )

To estimate sin ( 62 ° ) using L , we must first convert 62 ° to radians. We have 62 ° = 62 π 180 radians, so the estimate for sin ( 62 ° ) is given by

sin ( 62 ° ) = f ( 62 π 180 ) L ( 62 π 180 ) = 3 2 + 1 2 ( 62 π 180 π 3 ) = 3 2 + 1 2 ( 2 π 180 ) = 3 2 + π 180 0.88348 .
The function f(x) = sin x is shown with its tangent at (π/3, square root of 3 / 2). The tangent appears to be a very good approximation for x near π / 3.
The linear approximation to f ( x ) = sin x at x = π / 3 provides an approximation to sin x for x near π / 3 .
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Find the linear approximation for f ( x ) = cos x at x = π 2 .

L ( x ) = x + π 2

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Linear approximations may be used in estimating roots and powers. In the next example, we find the linear approximation for f ( x ) = ( 1 + x ) n at x = 0 , which can be used to estimate roots and powers for real numbers near 1. The same idea can be extended to a function of the form f ( x ) = ( m + x ) n to estimate roots and powers near a different number m .

Approximating roots and powers

Find the linear approximation of f ( x ) = ( 1 + x ) n at x = 0 . Use this approximation to estimate ( 1.01 ) 3 .

The linear approximation at x = 0 is given by

L ( x ) = f ( 0 ) + f ( 0 ) ( x 0 ) .

Because

f ( x ) = ( 1 + x ) n f ( 0 ) = 1 f ( x ) = n ( 1 + x ) n 1 f ( 0 ) = n ,

the linear approximation is given by [link] (a).

L ( x ) = 1 + n ( x 0 ) = 1 + n x

We can approximate ( 1.01 ) 3 by evaluating L ( 0.01 ) when n = 3 . We conclude that

( 1.01 ) 3 = f ( 1.01 ) L ( 1.01 ) = 1 + 3 ( 0.01 ) = 1.03 .
This figure has two parts a and b. In figure a, the line f(x) = (1 + x)3 is shown with its tangent line at (0, 1). In figure b, the area near the tangent point is blown up to show how good of an approximation the tangent is near (0, 1).
(a) The linear approximation of f ( x ) at x = 0 is L ( x ) . (b) The actual value of 1.01 3 is 1.030301. The linear approximation of f ( x ) at x = 0 estimates 1.01 3 to be 1.03.
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Find the linear approximation of f ( x ) = ( 1 + x ) 4 at x = 0 without using the result from the preceding example.

L ( x ) = 1 + 4 x

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Differentials

We have seen that linear approximations can be used to estimate function values. They can also be used to estimate the amount a function value changes as a result of a small change in the input. To discuss this more formally, we define a related concept: differentials . Differentials provide us with a way of estimating the amount a function changes as a result of a small change in input values.

When we first looked at derivatives, we used the Leibniz notation d y / d x to represent the derivative of y with respect to x . Although we used the expressions dy and dx in this notation, they did not have meaning on their own. Here we see a meaning to the expressions dy and dx . Suppose y = f ( x ) is a differentiable function. Let dx be an independent variable that can be assigned any nonzero real number, and define the dependent variable d y by

d y = f ( x ) d x .

It is important to notice that d y is a function of both x and d x . The expressions dy and dx are called differentials . We can divide both sides of [link] by d x , which yields

d y d x = f ( x ) .

This is the familiar expression we have used to denote a derivative. [link] is known as the differential form    of [link] .

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Source:  OpenStax, Calculus volume 1. OpenStax CNX. Feb 05, 2016 Download for free at http://cnx.org/content/col11964/1.2
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