Can't pass by without making a public service announcement about the average speed example: don't compute integrals using the calculus definition of Sum(f(x_i) * delta). Look up quadrature or numerical integration methods instead.
Although it is true that you can approximate the average speed by taking an average of instantaneous speed measurements, that's usually a very bad way to do it in any real world situation. Numerical difference values are always noisier than the underlying quantity, sometimes to the point of being unusable, so of course if you can just read off the quantity you want directly (total difference over time), you should do that. But even if you can't, you should use a proper integration method instead of the calculus definition.
I have seen the Sum(f(x_i) * delta) calculation in a lot of real-world code. It has bad convergence properties, bad errors when the function has large derivatives, and bad performance when the data has noise. Some of the code I've seen produces garbage results, or has thousands of function evaluations when you need, like, four. "Quadrature? I think I heard that before, but I don't remember what it means."
In summary, please don't compute derivatives as (f(x_i+1)-f(x_i))/delta, or compute integrals as Sum(f(x_i) * delta), and especially, please don't do the first immediately followed by the second. Which also happens. Look up numerical methods instead.
This has been a public service announcement.
Although it is true that you can approximate the average speed by taking an average of instantaneous speed measurements, that's usually a very bad way to do it in any real world situation. Numerical difference values are always noisier than the underlying quantity, sometimes to the point of being unusable, so of course if you can just read off the quantity you want directly (total difference over time), you should do that. But even if you can't, you should use a proper integration method instead of the calculus definition.
I have seen the Sum(f(x_i) * delta) calculation in a lot of real-world code. It has bad convergence properties, bad errors when the function has large derivatives, and bad performance when the data has noise. Some of the code I've seen produces garbage results, or has thousands of function evaluations when you need, like, four. "Quadrature? I think I heard that before, but I don't remember what it means."
In summary, please don't compute derivatives as (f(x_i+1)-f(x_i))/delta, or compute integrals as Sum(f(x_i) * delta), and especially, please don't do the first immediately followed by the second. Which also happens. Look up numerical methods instead.
This has been a public service announcement.