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GROWTH Function (LibreOffice Calc)

Math Intermediate LibreOffice Calc Introduction not established

The GROWTH function in LibreOffice Calc returns predicted Y-values based on exponential regression. This guide explains syntax, forecasting behavior, examples, errors, and best practices.

Verification status: Consult the documentation, examples and any stated test scope below. How formula examples are checked

Availability and version history

  • Introduced: Documented by LibreOffice 26.8.1.1; original introduction not established.
  • Availability: Supported in LibreOffice 26.8.1.1, verified against its function registry. This identifies a supported release, not a final working version.
  • Scope: Desktop Calc, using the English function name. Availability in other spreadsheet applications is separate.
  • Checked: 2026-10-14 — function reference; versioned support. Documentation/source check; not a new native test. Existing example-specific test claims below retain their stated scope.
The **GROWTH** function predicts Y-values using **exponential regression**, fitting data to the curve: 
y = b * m^x

It is the exponential counterpart to TREND and the practical forecasting interface for LOGEST. GROWTH is ideal for modeling compounding processes, population growth, financial projections, and scientific data.

What the GROWTH Function Does

  • Predicts Y-values based on an exponential regression model
  • Works with one or more independent variables
  • Supports forecasting future values
  • Works as an array function (spills in modern Calc)
  • Can fill missing values or extend exponential trends
  • Works across sheets

GROWTH is the easiest way to generate exponential forecasts.

Syntax

GROWTH(known_y; known_x; new_x; const)

Where:

  • known_y — dependent variable (must be positive)
  • known_x — independent variable(s)
  • new_x — X values to predict for
  • const — TRUE = calculate intercept; FALSE = force intercept = 1
If new_x is omitted, GROWTH predicts Y-values for the same X-values in known_x.

Basic Examples

Predict Y-values for the same X-range

=GROWTH(B1:B10; A1:A10)

Forecast future values

Predict Y for X = 11 to 20:

=GROWTH(B1:B10; A1:A10; A11:A20)

Forecast with intercept forced to 1

=GROWTH(B1:B10; A1:A10; A11:A20; FALSE)

Forecast across sheets

=GROWTH(Sheet1.B1:B50; Sheet2.A1:A50; Sheet2.A51:A60)

Advanced Examples

Fill missing values in an exponential dataset

=GROWTH(B1:B100; A1:A100; A1:A100)

Multi-variable exponential regression prediction

=GROWTH(Y1:Y20; X1:Z20; X21:Z30)

Predict using LOGEST coefficients (manual equivalent)

Base (m):

=INDEX(LOGEST(B1:B10; A1:A10); 1)

Intercept (b):

=INDEX(LOGEST(B1:B10; A1:A10); 1; 2)

Prediction:

=INDEX(LOGEST(B1:B10; A1:A10); 1; 2) * INDEX(LOGEST(B1:B10; A1:A10); 1) ^ X

GROWTH automates this.

GROWTH ignoring errors

=GROWTH(IF(ISNUMBER(B1:B10); B1:B10); A1:A10; A11:A20)

(Confirm with Ctrl+Shift+Enter in older Calc.)

GROWTH on visible cells only (filtered data)

Use SUBTOTAL helper column to filter X/Y before passing to GROWTH.

GROWTH for time-series forecasting

If A1:A10 contains dates:

=GROWTH(B1:B10; A1:A10; A11:A20)

GROWTH for compounding processes

=GROWTH(Population; Years; FutureYears)

How GROWTH Calculates Predictions

GROWTH uses the same exponential model as LOGEST:

y = b * m^x

Steps:

  1. Compute m (growth factor) and b (intercept) using exponential least squares
  2. For each new_x, compute:
    y = b * m^new_x
  3. Return an array of predicted values

If const = FALSE, the model becomes:

y = m^x

Requirements and Constraints

  • All Y values must be positive
  • X values may be any real numbers
  • Multi-variable regression requires matching row counts

Common Errors and Fixes

Err:502 — Invalid argument

Occurs when:

  • Y values contain zero or negative numbers
  • X and Y ranges have mismatched lengths
  • Non-numeric text included

Err:504 — Parameter error

Occurs when:

  • Semicolons are incorrect
  • Range references malformed

GROWTH returns only one value instead of many

Occurs when:

  • Output range too small in older Calc
  • Not entered as array formula (older versions)

GROWTH predictions look incorrect

Possible causes:

  • Relationship is not exponential
  • Outliers distort regression
  • Y-values include zeros or negatives

Best Practices

  • Use GROWTH for exponential forecasting and compounding processes
  • Use TREND for linear forecasting
  • Use LOGEST when you need full regression statistics
  • Remove or investigate outliers
  • Ensure Y values are strictly positive
  • Use named ranges for cleaner formulas
GROWTH is perfect for modeling compounding processes — population growth, financial projections, decay curves, and anything that accelerates or decelerates exponentially.