This section covers:

**Differentials**, **Linear Approximation** and **Error Propagation** are more applications of Differential Calculus.

# Differentials

Think of differentials of picking apart the “fraction” \(\frac{{dy}}{{dx}}\) we learned to use when differentiating a function.

We learned that the **derivative** or rate of change of a function can be written as \(\frac{{dy}}{{dx}}={f}’\left( x \right)\), where *dy* is an infinitely small change in *y*, and *dx* (or \(\Delta x\)) is an infinitely small change in *x*. So it turns out that if \(f\left( x \right)\) is a function that is differentiable on an open interval containing *x*, and the **differential of x** (

*dx*) is a non-zero real number, then \(dy={f}’\left( x \right)dx\) (see how we just multiplied both sides by

*dx*)? And I won’t get into this at this point, but the differential of

*y*can be used to approximate the change in

*y*, so \(\Delta y\approx dy\).

## Calculating Differentials

We learned **differentiation rules** earlier, and these apply to differentials too. These look familiar, right? We’ll see that we’ll need to use the **differential produce rule** in the problem **here**.

Here are the **differential formulas**:

# Linear Approximation

We can use differentials to perform linear approximations of functions (we did this here with **tangent line approximation**) with this formula that looks similar to a **point-slope formula** (remember that the derivative is a slope): \(y-{{y}_{0}}={f}’\left( {{{x}_{0}}} \right)\left( {x-{{x}_{0}}} \right)\), or \(f\left( x \right)-f\left( {{{x}_{0}}} \right)={f}’\left( {{{x}_{0}}} \right)\left( {x-{{x}_{0}}} \right)\), which means \(f\left( x \right)=f\left( {{{x}_{0}}} \right)+{f}’\left( {{{x}_{0}}} \right)\left( {x-{{x}_{0}}} \right)\). And remember that the variables with subscript “0” are the “old” values. So think of the equation as the “new *y*” equals the “old *y*” plus the derivative at the “old *x*” times the difference between the “new *x*” and the “old *x*”.

(And remember that we do these types of problems so we can “appreciate math” the way those used calculus before calculators and computers.)

Here are some examples in both finding **differentials** and finding **approximations of functions**:

# Error Propagation

We can also use **differentials** in Physics to **estimate errors**, say in physical measuring devices. In these problems, we’ll typically take a derivative, and use the “*dx*” or “*dy*” part of the derivative as the error. Then, to get **percent error**, we’ll divide the error by the total amount and multiply by 100.

The other thing to remember is that when we are solving for an error, it can go either way, so we typically express our answers with a “**±**”.

We’ll attack these problems the same way we did with **related rates** problems: write down what we know, what we need, and how we relate the variables.

Here are some problems:

Here are a few more that are a bit more difficult; for the first below, we need to use the **Differentials Product Rule**:

**Learn these rules and practice, practice, practice!**

On to **Exponential and Logarithmic Differentiation** — you are ready!