# STEYX

The STEYX function calculates the standard error of the predicted y-values for each x in the regression of a dataset.

## Syntax ðŸ”—

=STEYX(`known_y's`

, `known_x's`

)

`known_y's` | The y-values in the data set. |

`known_x's` | Optional. The x-values in the data set. If omitted, the x-values are assumed to be 1, 2, 3, ... (an array from 1 to the number of y-values). |

## About STEYX ðŸ”—

In the world of data analysis within Excel, the STEYX function proves to be invaluable for computing the standard error of the predicted y-values associated with each x in a dataset. This function is particularly beneficial when performing regression analysis on a set of data points, allowing for a deeper understanding of the predictive accuracy of the regression model. By utilizing the STEYX function, users can gain insights into the dispersion of predicted values around the regression line and evaluate the reliability of predictions made by the regression model against actual data points. When faced with the need to assess the precision of predicted values in relation to the actual observations within a dataset, investing time in mastering STEYX can lead to more informed decision-making and enhanced analytical capabilities.

## Examples ðŸ”—

Consider a dataset where you have actual y-values in cells A2:A10 and predicted y-values in cells B2:B10. If you want to calculate the standard error of predicted y-values, the STEYX formula would be: =STEYX(A2:A10, B2:B10)

If you have a dataset with the y-values in cells C2:C8 and do not have corresponding x-values, Excel will assume default x-values from 1 to the number of y-values provided. In this case, the STEYX formula would be: =STEYX(C2:C8)

## Notes ðŸ”—

Ensure that the known_y's and known_x's inputs are arrays of numeric values or references to cells containing numeric values. The STEYX function is a powerful tool for assessing the standard error of predicted y-values based on a regression model, providing users with essential information to evaluate the predictive accuracy of the model.

## Questions ðŸ”—

**What does the standard error of the predicted y-values signify in the context of regression analysis?**

The standard error of the predicted y-values, calculated using the STEYX function, indicates the average distance between the observed y-values and the predicted y-values generated by the regression model. It serves as a measure of how well the regression model predicts the actual data points.

**Can I use the STEYX function without providing x-values?**

Yes, you can use the STEYX function without specifying x-values. In such cases, Excel automatically assigns default x-values of 1, 2, 3, and so on up to the number of provided y-values to perform the standard error calculation.

**How can the standard error calculated by the STEYX function aid in decision-making processes?**

By gauging the standard error of the predicted y-values, analysts and decision-makers can assess the reliability of predictions made by a regression model. This information assists in evaluating the accuracy of the model's predictions and guides more informed decision-making based on the model's predictive capabilities.