STEYX function

STEYX function This article describes the formula syntax and usage of the STEYX  function in Microsoft Excel. ...

STEYX function

This article describes the formula syntax and usage of the STEYX function in Microsoft Excel.

Description

Returns the standard error of the predicted y-value for each x in the regression. The standard error is a measure of the amount of error in the prediction of y for an individual x.

Syntax

STEYX(known_y's, known_x's)

The STEYX function syntax has the following arguments:

  • Known_y's    Required. An array or range of dependent data points.

  • Known_x's    Required. An array or range of independent data points.

Remarks

  • Arguments can either be numbers or names, arrays, or references that contain numbers.

  • Logical values and text representations of numbers that you type directly into the list of arguments are counted.

  • If an array or reference argument contains text, logical values, or empty cells, those values are ignored; however, cells with the value zero are included.

  • Arguments that are error values or text that cannot be translated into numbers cause errors.

  • If known_y's and known_x's have a different number of data points, STEYX returns the #N/A error value.

  • If known_y's and known_x's are empty or have less than three data points, STEYX returns the #DIV/0! error value.

  • The equation for the standard error of the predicted y is:

    This article describes the formula syntax and usage of the  STEYX function

    where x and y are the sample means AVERAGE(known_x's) and AVERAGE(known_y's), and n is the sample size.

Example

Copy the example data in the following table, and paste it in cell A1 of a new Excel worksheet. For formulas to show results, select them, press F2, and then press Enter. If you need to, you can adjust the column widths to see all the data.

Data

Known y

Known x

2

6

3

5

9

11

1

7

8

5

7

4

5

4

Formula

Description (Result)

Result

=STEYX(A3:A9,B3:B9)

Standard error of the predicted y-value for each x in the regression (3.305719)

3.305719

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