The `STEYX` function in Excel is used to calculate the standard error of the predicted y-value for each x in the regression, which is essentially the standard error of the estimate for the dependent variable. This function is useful in regression analysis to determine how well the predicted values will match real data points.
Syntax
STEYX(known_y's, known_x's)
- known_y’s: This argument represents the range of dependent data points in your data set. These are the observed values corresponding to each x-value.
- known_x’s: This argument represents the range of independent data points.
Steps to Use `STEYX` Function
=STEYX(B2:B10, A2:A10)
- Prepare Your Data: Ensure your data is organized, with known x-values and corresponding y-values in separate columns.
- Select a Cell for the Result: Click on the cell where you wish to display the standard error of the y-estimate.
- Enter the Function: You can type the formula directly in the cell or use the formula bar. Enter it in the following format:
Here, `B2:B10` represents the range containing your known y-values, and `A2:A10` represents the range containing your known x-values. Adjust these ranges according to your data.
- Press Enter: Once you’ve input the function and assigned the correct cell ranges, press `Enter`. Excel will calculate and display the standard error of the y-estimate in the selected cell.
Example
Assume you have a small data set with your x-values in column A (from A2 to A10) and corresponding y-values in column B (from B2 to B10). By using the formula `=STEYX(B2:B10, A2:A10)`, Excel will compute the standard error of the estimate for the regression line that best fits that data.
Important Notes
- Number of Points: Make sure that both `known_y’s` and `known_x’s` arrays have the same number of data points. If they don’t, Excel will return a `#N/A` error.
- Data Type: The data should be numeric; non-numeric values will cause errors.
- Missing Values: Any empty cells in the range will also result in errors, so make sure your data ranges are complete.
Therefore, use the `STEYX` function when you need a statistical measure of how accurately your regression equation predicts the dependent variable values.
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