Statistical
STEYX Function in Excel
Returns the standard error of the predicted y-value for each x in the regression.
Syntax
- =STEYX(known_y's, known_x's)
Arguments
- known_y's (required): Dependent values
- known_x's (required): Independent values
Examples
- =STEYX(B1:B10, A1:A10) - Standard error of estimate - Result: Error value
STEYX for data analysis
- Confirm whether you need entire columns, filtered subsets, or distinct values.
- Use [COUNTIFS](/functions/countifs/) and [SUMIFS](/functions/sumifs/) for multi-criteria metrics.
- Pivot tables complement single-cell statistical formulas for exploration.
Common errors
- Arrays must be same size
Use cases
- Regression accuracy
- Prediction intervals
- Model evaluation
Frequently asked questions
- What is STEYX in Excel? STEYX calculates the standard error of the estimate in linear regression - the typical distance between actual Y values and predicted Y values. Lower STEYX means better predictions. It measures the scatter of data points around the regression line.
- How do I use STEYX for prediction intervals? For approximate 95% prediction interval: predicted_y ± 2*STEYX. Example: if TREND predicts 100 and STEYX is 5, expect actual values roughly between 90-110 (100±10). For precise intervals, use t-distribution with appropriate degrees of freedom.
- What's a good STEYX value? STEYX should be evaluated relative to your Y values. Compare STEYX to the mean or range of Y. If mean(Y)=100 and STEYX=5, that's 5% error - probably good. If mean(Y)=10 and STEYX=5, that's 50% error - poor fit. Also compare to STDEV of Y.
Editorial review
- Reviewed by Excel.Directory Editorial Team. Updated May 2026.