In the linear regression, you want the predicted values to be close to the actual values. So to have a good fit, that plot should resemble a straight line at 45 degrees. However, here the predicted values are larger that the actual values over the range of 10-20. This means that you are over estimating. Therefore, the mode does not seem to provide an adequate fit and should be revised.

In the linear regression, you want the predicted values to be close to the actual values. So to have a good fit, that plot should resemble a straight line at 45 degrees. However, here the predicted values are larger than the actual values over the range of 10-20. This means that you are over-estimating. Therefore, the model does not seem to provide an adequate fit and should be revised.

In the linear regression, you want the predicted values to be close to the actual values. So that plot should resemble a straight line at 45 degrees.

In the linear regression, you want the predicted values to be close to the actual values. So to have a good fit, that plot should resemble a straight line at 45 degrees. However, here the predicted values are larger that the actual values over the range of 10-20. This means that you are over estimating. Therefore, the mode does not seem to provide an adequate fit and should be revised.