# STEYX

The `STEYX` function returns the standard error of the predicted y-value for each x in the regression of the data set. It is commonly used in statistical analysis to evaluate the accuracy of a regression line. The function requires two arrays of data, one for the y-values and one for the x-values.

## Usage

Use the `STEYX` formula with the syntax shown below, it has 2 required parameters:

Parameters:
1. data_y (required):
An array or range containing the dependent variable values.
2. data_x (required):
An array or range containing the independent variable values.

## Examples

Here are a few example use cases that explain how to use the `STEYX` formula in Google Sheets.

### Evaluating the accuracy of a regression line

By using the `STEYX` function, you can determine how closely the predicted y-values from a regression line match the actual y-values of the data set.

### Identifying outliers

Large standard errors of prediction (as calculated by `STEYX`) for certain x-values can indicate outliers in the data set.

### Comparing regression models

By comparing the standard errors of prediction for different regression models, you can determine which model provides the most accurate predictions for a given data set.

## Common Mistakes

`STEYX` not working? Here are some common mistakes people make when using the `STEYX` Google Sheets Formula:

### Using incorrect data types for data_y and data_x

Make sure that the data_y and data_x parameters are arrays or ranges of numerical data.

### Providing data in the wrong order

Make sure that the data_y parameter is the dependent variable and the data_x parameter is the independent variable.

The following functions are similar to `STEYX` or are often used with it in a formula:

• `STDEV.S`

The `STDEV.S` formula calculates the standard deviation of a sample of data. It is commonly used to measure the amount of variation or dispersion of a set of values from their average. This formula accepts up to 255 arguments, which can be numbers, arrays, or references that contain numbers. The result is a measure of how spread out the data is, with a larger standard deviation indicating greater variability.

• `SLOPE`

The `SLOPE` formula calculates the slope of the linear regression line that best fits the input data. It is commonly used in statistics to analyze trends and predict future values based on past performance.

• `INTERCEPT`

The `INTERCEPT` function calculates the point where the line of best fit for a set of data intercepts the y-axis. This function is commonly used in regression analysis to find the constant b in the equation y=mx+b where m is the slope of the regression line.

You can learn more about the `STEYX` Google Sheets function on Google Support.