## Rectangular Integration (a.k.a. The Midpoint Rule) – Conceptual Foundations and a Statistical Application in R

#### Introduction

Continuing on the recently born series on numerical integration, this post will introduce rectangular integration.  I will describe the concept behind rectangular integration, show a function in R for how to do it, and use it to check that the $Beta(2, 5)$ distribution actually integrates to 1 over its support set.  This post follows from my previous post on trapezoidal integration.

Image courtesy of Qef from

#### Conceptual Background of Rectangular Integration (a.k.a. The Midpoint Rule)

Rectangular integration is a numerical integration technique that approximates the integral of a function with a rectangle.  It uses rectangles to approximate the area under the curve.  Here are its features:

• The rectangle’s width is determined by the interval of integration.
• One rectangle could span the width of the interval of integration and approximate the entire integral.
• Alternatively, the interval of integration could be sub-divided into $n$ smaller intervals of equal lengths, and $n$ rectangles would used to approximate the integral; each smaller rectangle has the width of the smaller interval.
• The rectangle’s height is the function’s value at the midpoint of its base.
• Within a fixed interval of integration, the approximation becomes more accurate as more rectangles are used; each rectangle becomes narrower, and the height of the rectangle better captures the values of the function within that interval.

## Trapezoidal Integration – Conceptual Foundations and a Statistical Application in R

#### Introduction

Today, I will begin a series of posts on numerical integration, which has a wide range of applications in many fields, including statistics.  I will introduce trapezoidal integration by discussing its conceptual foundations, write my own R function to implement trapezoidal integration, and use it to check that the Beta(2, 5) probability density function actually integrates to 1 over its support set.  Fully commented and readily usable R code will be provided at the end.

Given a probability density function (PDF) and its support set as vectors in an array programming language like R, how do you integrate the PDF over its support set to ensure that it equals to 1?  Read the rest of this post to view my own R function to implement trapezoidal integration and learn how to use it to numerically approximate integrals.