Both choices influence the estimate. Dividing the interval into narrower subintervals creates more rectangles and generally improves accuracy because each rectangle represents a smaller portion of the curve. The selected point within each subinterval determines the rectangle’s height, so changing from a left endpoint to a right endpoint or midpoint can produce a different numerical result.
The three rules differ only in where each rectangle’s height is sampled. The left rule uses the beginning of each subinterval, the right rule uses its end, and the midpoint rule samples halfway between them. Comparing these results reveals how sensitive the estimated area is to sample-point placement and helps quantify approximation error.
More rectangles mean narrower subintervals, allowing the collection of rectangular tops to follow the curve more closely across the interval. Each individual rectangle then represents a smaller region, reducing the effect of local differences between the curve and the approximation. This refinement is why decreasing subinterval widths generally produces a more accurate area estimate.
First, determine the width of each subinterval and select one sample point within it. Evaluate the function at that point to obtain the rectangle’s height, then multiply height by width to find its area. Adding the areas of all rectangles produces the Riemann sum, which serves as the numerical estimate.
It is especially useful when an exact antiderivative is difficult or unavailable. Instead of requiring a symbolic integration formula, the method uses function values at selected points and combines the resulting rectangle areas. This makes numerical integration practical for estimating accumulated quantities from a function represented through values or a graph.
The method connects a graph to a numerical estimate of the region beneath it, helping students and researchers interpret how function values accumulate across an interval. In modeling, the resulting sum can approximate a total quantity when an exact calculation is impractical. Refining the partition also provides a way to examine the estimate’s reliability.