Probable Error

The ratio of coefficient of non-determination and the square root of the number of observations is known as the probable error.

Explanation:
- The probable error is a statistical term that helps in determining the reliability of the regression model.
- It is calculated by dividing the coefficient of non-determination (1 - R^2) by the square root of the number of observations (n).
- The formula for probable error is PE = sqrt((1 - R^2) / n).

Significance:
- The probable error provides an estimate of the standard deviation of the errors in the regression model.
- It helps in understanding the accuracy and precision of the regression model predictions.
- A lower probable error indicates a more reliable regression model.

Usage:
- Researchers and analysts use the probable error to assess the quality of the regression model and make informed decisions based on the model's predictive power.
- It is a useful tool in evaluating the goodness-of-fit of the regression model and determining the level of uncertainty in the predictions.

In conclusion, the probable error is a valuable statistical measure that aids in assessing the reliability and accuracy of regression models. By calculating the ratio of the coefficient of non-determination to the square root of the number of observations, researchers can gain insights into the probable standard deviation of errors in the model.