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RSQ Function (LibreOffice Calc)

Math Beginner LibreOffice Calc Introduction not established

The RSQ function in LibreOffice Calc returns the coefficient of determination (R²) between two datasets. This guide explains syntax, interpretation, 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 **RSQ** function returns the **coefficient of determination (R²)** between two datasets. R² measures how well a linear regression model explains the variation in the dependent variable.

What the RSQ Function Does

  • Calculates R², the coefficient of determination
  • Measures how well X explains Y in a linear model
  • Works with numeric X/Y pairs
  • Useful for regression diagnostics and model evaluation
  • Works across sheets

R² is always between 0 and 1.

Syntax

RSQ(known_y; known_x)

Where:

  • known_y — dependent variable (Y values)
  • known_x — independent variable (X values)
RSQ = PEARSON² = CORREL²

Interpretation of R²

R² Value Meaning
1.0 Perfect fit
0.9–1.0 Excellent fit
0.7–0.9 Strong fit
0.5–0.7 Moderate fit
0.3–0.5 Weak fit
0–0.3 Very weak fit

R² measures how much of the variance in Y is explained by X.

Basic Examples

R² for a simple regression

=RSQ(B1:B10; A1:A10)

R² across sheets

=RSQ(Sheet1.B1:B50; Sheet2.A1:A50)

R² using named ranges

=RSQ(Sales; MarketingSpend)

R² with dates as X-values

=RSQ(B1:B100; A1:A100)

(Calc converts dates to serial numbers.)

Advanced Examples

R² ignoring errors

=RSQ(IF(ISNUMBER(B1:B100); B1:B100); IF(ISNUMBER(A1:A100); A1:A100))

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

R² using filtered (visible) data only

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

R² after removing outliers

=RSQ(FILTER(B1:B100; B1:B100<1000); FILTER(A1:A100; B1:B100<1000))

R² for log-transformed data

=RSQ(LN(B1:B10); LN(A1:A10))

R² for exponential regression (manual)

Exponential model:

y = b * m^x

Equivalent linearized R²:

=RSQ(LN(Y1:Y20); X1:X20)

R² for multi-variable regression (via LINEST)

=INDEX(LINEST(Y1:Y20; X1:Z20; TRUE; TRUE); 3; 1)

How RSQ Calculates the Coefficient of Determination

RSQ is simply:

[ R^2 = r^2 ]

Where:

  • ( r ) = Pearson correlation coefficient

Expanded:

[ R^2 = \frac{\left[\sum (x_i - \bar{x})(y_i - \bar{y})\right]^2}{\left[\sum (x_i - \bar{x})^2\right]\left[\sum (y_i - \bar{y})^2\right]} ]

R² measures the proportion of variance in Y explained by X.

Common Errors and Fixes

Err:502 — Invalid argument

Occurs when:

  • X and Y ranges have different sizes
  • One or both arrays contain no numeric values
  • Arrays contain only one data point

Err:504 — Parameter error

Occurs when:

  • Semicolons are incorrect
  • Range references malformed

RSQ returns unexpected value

Possible causes:

  • Relationship is non-linear
  • Outliers distort regression
  • X-values or Y-values contain hidden text
  • Data contains zeros that should be excluded

RSQ differs from LINEST R²

They are identical — LINEST simply returns more statistics.

Best Practices

  • Use RSQ to evaluate regression model quality
  • Use CORREL or PEARSON to measure raw correlation
  • Use LINEST for full regression diagnostics
  • Remove outliers before modeling
  • Plot your data to confirm linearity
  • Use named ranges for cleaner formulas
RSQ is the quickest way to judge whether your regression model is meaningful — a high R² means your predictor actually explains the behavior of your data.