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Today:
Left-endpoint Rectangle Rule: \[ \int_a^b f(x) dx \approx LRR_n = {\Delta x}[f(x_0)+f(x_1)+f(x_2)+\cdots+f(x_{n-1})] \] Right-endpoint Rectangle Rule: \[ \int_a^b f(x) dx \approx RRR_n = {\Delta x}[f(x_1)+f(x_2)+\cdots+f(x_{n-1})+f(x_n)] \] Midpoint Rule: \[ \int_a^b f(x) dx \approx M_n = \Delta x [f(\overline{x_1})+f(\overline{x_2})+\cdots+f(\overline{x_n}) \] where $\overline{x_i}=\frac{1}{2}(x_{i-1}+x_i)=$ midpoint of $[x_{i-1},x_i]$.
(their arithmetic average, in this case).
Trapezoidal Rule: \[ \int_a^b f(x) dx \approx T_n = \frac{\Delta x}{2} [f(x_0)+2f(x_1)+2f(x_2)+\cdots++2f(x_{n-1})+f(x_n)] \] where \[ \Delta x = \frac{b-a}{n} \] and $x_i=a + i \Delta x$.
Notice that the number of subintervals in Simpson's rule must be even.
Now we can go further:
(their weighted arithmetic averages).
Here are the error bounds, that illustrate that the errors of midpoint and trapezoidal are related, and suggest how to combine them to create a better method (Simpson's rule):