Regression model analysis is done by RMSE(Root Mean Square Error) value.
An RMSE of zero, meaning that the estimator
predicts observations of the parameter 
with perfect accuracy, is the ideal, but is practically never possible.
The regression line predicts the average y value associated with a given x value. Note that is also necessary to get a measure of the spread of the y values around that average. To do this, we use the root-mean-square error (r.m.s. error).
To construct the r.m.s. error, you first need to determine the residuals. Residuals are the difference between the actual values and the predicted values. It is denoted by
is the observed value for the ith observation and
is the predicted value.
They can be positive or negative as the predicted value under or over estimates the actual value. Squaring the residuals, averaging the squares, and taking the square root gives us the r.m.s error. You then use the r.m.s. error as a measure of the spread of the y values about the predicted y value.
An RMSE of zero, meaning that the estimator
predicts observations of the parameter with perfect accuracy, is the ideal, but is practically never possible.The regression line predicts the average y value associated with a given x value. Note that is also necessary to get a measure of the spread of the y values around that average. To do this, we use the root-mean-square error (r.m.s. error).
To construct the r.m.s. error, you first need to determine the residuals. Residuals are the difference between the actual values and the predicted values. It is denoted by
They can be positive or negative as the predicted value under or over estimates the actual value. Squaring the residuals, averaging the squares, and taking the square root gives us the r.m.s error. You then use the r.m.s. error as a measure of the spread of the y values about the predicted y value.
