Hi @MikeKim : I will try to address these questons.
Fig 2: this is at it should be.
Fig 3: this is as it should be (the Fit Model platform does not show the confidence intervals)
1. How do I consolidate the fig3 left into one graph containing 3 lines and respective CI?? You will have save the predictions and intervals to your data table and use graph builder. That will be the same as Figure 2 left..
2. How do I draw CI at the fig 3 right? You will have save the predictions and intervals to your data table and use graph builder.
3. How do I apply the Pooled MSE to the fig 3 left and fig 3 right? (to the same figure as fig 2 right). Again, you will have save the predictions and intervals to your data table and use graph builder.
4. There is Save Column option (in red triangle) in fig 3 right (to see the CI value in table)
However, they always fixed to alpha=0.05, in this case I need alpha=0.1 since the original Degradation shows 0.1 CI.
So, how do I change alpha 0.05 to 0.1 in fig 3 right? (please check this at fig 4 below). In the Fit Model dialog, there is a red triangle next to Model Specification. If you click on that you can "set alpha level".
5. What is the practical explanation (practical meaning in laboratorial field) of Checking the Use Pooled MSE?
(The answer to the question from the colleague, "what is the using pooled mse?"). I explained MSE pooling in my initial response. The MSE is the Mean Squared Error. It is an estimate of the variance, and the variance is the
(Standard Deviation)^2. Each line has it's own MSE. The MSE for each line is the (Sum of Squared Errors)/(N-2). The Sum of Squared Errors (SSE) is the sum of squared differences between the line and the data. SSE is the sum of squared residuals. See plot below.
So, if the spread of data around the line is tight, then MSE will be very small (like the blue batch in fig 2). If the spread of data around the line is larger, then MSE will be larger (like the red batch in fig 2).
Pooling the MSE's can be thought of taking an average of the MSE's to attain a "pooled MSE" for the purpose of calculating confidence intervals etc. If you don't pool, each batch will use it's own MSE to calculate the confidence intervals via the equation below.
So, is it best to pool? Well, it depends. From a statistical perspective, if the MSE's are similar, then pool. If they are not similar, then you should try to understand why. Looking at your data, the blue batch seems much less variable (much lower MSE than the other batches) so pooling may not be appropriate. If you pool the MSE's, the width of the three intervals (around each batch) will be the same (each using the pooled, or "average", MSE), and will not reflect the fact that the blue batch is less variable. If you don't use pooled MSE, then the blue batch will have much tighter interval (as shown in figure 3 left) because it uses it's own MSE.
I hope I've understood your questions, I hope I've been helpful...and I hope there aren't too many typos!