STEYX function in Excel

Jaw STEYX In Excel, the calculation function helps you to get the standard error of the predicted value of y for each value of x in the regression.

STEYX function in Excel

How to use STEYX Function in Excel

1. Description and syntax used

This is the function that returns the standard error of the predicted value of y for each value of x in the regression.

Syntax:

STEYX (known_y’s, known_x’s)

Inside:

– Known_y’s: Array, or dependent data point range, is a required parameter.

– Known_x’s: An independent data point or range, which is a required parameter.

Attention:

– Arguments can be numbers, names or arrays that contain numbers.

– When typing logical values ​​and presenting numeric values ​​into function arguments -> these values ​​are still calculated, but cells with 0 value will still be counted.

– Function error occurs when arguments are text or error values ​​cannot be converted.

– If Known_y’s and Known_x’s There are different data points -> The function returns the error value # N / A.

– If Known_y’s and Known_x’s empty or have fewer than three data points -> function returns error value # DIV / 0.

– The equation for standard errors is:

Inside:

+ x is the sample mean AVERAGE (number1, number2, …)

+ n is the sample size.

2. Examples of specific situations

Suppose you need to calculate the standard error of the predicted y value for each x value in the regression of the values ​​in the data table below:

Step 1: In the cell to calculate -> you enter the formula: = STEYX (B3: B8, C3: C8)

Step 2: Press Enter -> The standard error of the predicted value of y for each value of x in the regression is:

Case of data points of Know_y’s and Know_x’s different -> function returns error value # N / A.

Case of data points of Know_y’s and Know_x’s less than 3 data points -> function returns error value # DIV / 0.

So Taimienphi.vn just gave you detailed instructions on how to use it STEYX function in Excel. This is the function that returns the standard error of the predicted value of y for each value of x in the regression. Hopefully this article will help you in your studies and work. Good luck!

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