Calculate the complementary ERF function.

`ERFC(X)`

- X - required, lower bound for integrating

`=ERFC(1)`

The ERFC function is used to calculate the complementary error function of a number. This means that it calculates the area under the normal distribution curve to the right of a specified value. For example, this formula calculates the area under the normal distribution curve to the right of 1. The result is 0.157299207050287.

The ERFC function can also be used to calculate the probability of a value being greater than a certain number. For example, if you want to calculate the probability of a value being greater than 1, you can use the ERFC function with an argument of 1. The result is 0.842700792949713, which is the probability of a value being greater than 1.

The ERFC function can also be used to calculate the probability of a value being less than a certain number. For example, if you want to calculate the probability of a value being less than 1, you can use the ERFC function with an argument of -1. The result is 0.842700792949713, which is the probability of a value being less than 1.

The ERFC function can also be used to calculate the probability of a value being between two numbers. For example, if you want to calculate the probability of a value being between 1 and 2, you can use the ERFC function with an argument of 1 and -2. The result is 0.682689492137086, which is the probability of a value being between 1 and 2.

The ERFC function is a single argument function that integrates between x and infinity, returning the complementary ERF function. It requires an X argument as the lower bound.

- The ERFC function only takes numeric arguments.

The ERFC Function (Error Function Complement) is an integration function used to integrate between x and infinity.

The ERFC function only has one parameter, x.

The ERFC Function is used to integrate between x and infinity.

- Calculating probabilities in statistics
- Computing integrals in calculus
- Solving differential equations in engineering
- Measuring the probability that a variable is within a given range

- It is a more accurate and efficient way to compute integrals over a large range.
- It can be used to solve complex equations quickly.
- It can be used to calculate probabilities and measure the likelihood of a variable being within a given range.