Introduction
Error propagation can be a fair bit of work with a calculator, but it’s easy with R. Just use R in repeated calculation with perturbed quantities, and inspect the range of results. Two methods are shown below for inspecting the range: sd()
and quantile()
, the latter using the fact that area under a normal distribution is 0.68 when calculated between 1 and 1 standard deviation.
Tests
Case 1: scale factor
In this case, the answer is simple. If A
has uncertainty equal to 0.1, then 10A
has uncertainty 1.0.

set.seed(123)
n < 500
result < vector("double", n)
A < 1
Au < 0.1 # uncertainty in A
for (i in 1:n) {
Ap < A + Au * rnorm(n = 1)
result[i] = 10 * Ap
}
D < 0.5 * (1  0.68)
r < quantile(result, probs = c(D, 1  D))
cat("value:", mean(result), "uncertainty:", sd(result), " range:", r[1], "to",
r[2], "\n")

## value: 10.03 uncertainty: 0.9728 range: 9.047 to 11.02

hist(result)

The graph indicates that the values are symmetric, which makes sense for a linear operation.
Case 2: squaring
Here, we expect an uncertainty of 20 percent.

set.seed(123)
n < 500
result < vector("double", n)
A < 1
Au < 0.1 # uncertainty in A
for (i in 1:n) {
Ap < A + Au * rnorm(n = 1)
result[i] = Ap^2
}
D < 0.5 * (1  0.68)
r < quantile(result, probs = c(D, 1  D))
cat("value:", mean(result), "uncertainty:", sd(result), " range:", r[1], "to",
r[2], "\n")

## value: 1.016 uncertainty: 0.1965 range: 0.8184 to 1.213

hist(result)

Case 3: a nonlinear function
Here, we have a hyperbolic tangent, so the expected error bar will be trickier analytically, but of course the R method remains simple. (Note that the uncertainty is increased to ensure a nonlinear range of hyperbolic tangent.)

set.seed(123)
n < 500
result < vector("double", n)
A < 1
Au < 0.5 # uncertainty in A
for (i in 1:n) {
Ap < A + Au * rnorm(n = 1)
result[i] = tanh(Ap)
}
D < 0.5 * (1  0.68)
r < quantile(result, probs = c(D, 1  D))
cat("value:", mean(result), "uncertainty:", sd(result), " range:", r[1], "to",
r[2], "\n")

## value: 0.7009 uncertainty: 0.233 range: 0.4803 to 0.9065

hist(result)

Conclusions
The computation is a simple matter of looping over a perturbed calculation. Here, gaussian errors are assumed, but other distributions could be used (e.g. quantities like kinetic energy that cannot be distributed in a Gaussian manner).
Further work

How large should
n
be, to get results to some desired resolution? 
If the function is highly nonlinear, perhaps the
mean(result)
should be replaced bymedian(result)
, or something.
Resources
 R source code: 20140305errorbarsinr.R
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