ERF.PRECISE Function (LibreOffice Calc)

• Computes the error function
• Provides higher precision than ERF
• Used in probability, statistics, Gaussian modeling, and engineering
• Supports any real input

ERF.PRECISE(x)

Arguments

• x:
  The value at which to evaluate the error function.

[ \text{ERF}(x) = \frac{2}{\sqrt{\pi}} \int_0^x e^{-t^2} , dt ]

ERF.PRECISE uses a more stable numerical method than ERF.

Standard error function

=ERF.PRECISE(1)
→ 0.842700792949715

Negative input

=ERF.PRECISE(-1)
→ -0.842700792949715

Zero input

=ERF.PRECISE(0)
→ 0

Large positive input

=ERF.PRECISE(5)
→ extremely close to 1

Convert to normal CDF

=0.5 * (1 + ERF.PRECISE(x / SQRT(2)))

Probability between two z‑scores

=0.5 * (ERF.PRECISE(b / SQRT(2)) - ERF.PRECISE(a / SQRT(2)))

Diffusion/heat‑transfer modeling

=ERF.PRECISE(x / (2 * SQRT(D * t)))

Smooth activation function

=0.5 * (1 + ERF.PRECISE((A1 - threshold) / width))

Use in error propagation models

=ERF.PRECISE(A1 / (SQRT(2) * sigma))

ERF.PRECISE returns a number between –1 and 1

Behavior details

• ERF.PRECISE(∞) → 1
• ERF.PRECISE(–∞) → –1
• More stable for large |x| than ERF
• Guaranteed consistent results across platforms
• Accepts any real number

Invalid input → Err:502

• Use ERF.PRECISE when accuracy matters, especially for large |x|
• Use ERFC.PRECISE for complementary tail probabilities
• Convert to normal CDF using standard transformations
• Use in engineering models requiring stable numerical behavior
• Document scaling and units in physical models

• ERF — standard error function
• ERFC / ERFC.PRECISE — complementary error functions
• NORMDIST / NORMSDIST — normal distribution functions
• EXP / SQRT / PI — supporting math
• Custom integrals — advanced modeling

By mastering ERF.PRECISE, you can build robust, high‑precision statistical and engineering models in LibreOffice Calc.