What I want is the uncertainty in the coefficients

In summary, the conversation discusses finding the uncertainty in the coefficients of a fitted 8th order polynomial for a set of data from an ion engine experiment. One method mentioned is using the design matrix and the equation C_{ij}=(A^T A)^{-1}. The square root of the diagonals of the resulting matrix should give the uncertainties, but it is not possible to find the inverse if the matrix is not square. However, it is pointed out that (A^T A) is square and invertible. The individual then mentions some issues they were having with finding the uncertainties, but ultimately they were able to find the covariance matrix and associated uncertainties.
  • #1
Winzer
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0

Homework Statement


I was given a set of data from a Ion engine experiment. I fitted the data with an 8th order polynomial that seems pretty reasonable. What I want is the uncertainty in the coefficients.
One way I read was through the design matrix.

Homework Equations


[tex] C_{ij}=(A^T A)^{-1} [/tex]

The Attempt at a Solution


The square root of the diagonals give the uncertainties. But my matrix in not invertible since its not square.
What am I missing?
 
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  • #2
Winzer said:

Homework Statement


I was given a set of data from a Ion engine experiment. I fitted the data with an 8th order polynomial that seems pretty reasonable. What I want is the uncertainty in the coefficients.
One way I read was through the design matrix.

Homework Equations


[tex] C_{ij}=(A^T A)^{-1} [/tex]


The Attempt at a Solution


The square root of the diagonals give the uncertainties. But my matrix in not invertible since its not square.
What am I missing?

The matrix A may not be square, but

[tex] (A^T A) [/tex]

is square, so it's invertible.
 
  • #3
The Electrician said:
The matrix A may not be square, but

[tex] (A^T A) [/tex]

is square, so it's invertible.
Thank you. Yes it turns out square but the inverse gives me problems.
Maybe I am using the command wrong.
Here are the results of a test.

I threw in .1 just for error sake. My model is a+bx+cx^2. The uncertainties are suppose to be given by the square root of the diagonals of
[tex](A^T A)^{-1} [/tex] A being the design matrix.
 

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  • #4
Never mind I got it.
I was able to find the covariance matrix and the associated uncertainties. I feel stupid.
 

1. What is the uncertainty in coefficients?

The uncertainty in coefficients refers to the amount of error or variation in the estimated values of the coefficients in a statistical model. It is a measure of how well the model fits the data and how reliable the estimated coefficients are.

2. How is the uncertainty in coefficients calculated?

The uncertainty in coefficients is typically calculated through statistical methods such as confidence intervals or standard errors. These methods take into account the variability of the data and the sample size to determine the range of possible values for the coefficients.

3. Why is it important to consider the uncertainty in coefficients?

Considering the uncertainty in coefficients is important because it provides a measure of the reliability and accuracy of the coefficients in a statistical model. It helps to determine the level of confidence we can have in the results and make informed decisions based on the model.

4. How does the uncertainty in coefficients affect the overall model?

The uncertainty in coefficients can affect the overall model by influencing the significance of the coefficients and the overall fit of the model. If the uncertainty is high, it may indicate that the model is not a good fit for the data and the coefficients should be interpreted with caution.

5. Can the uncertainty in coefficients be reduced?

Yes, the uncertainty in coefficients can be reduced by increasing the sample size and improving the quality of the data. Additionally, using more advanced statistical techniques and addressing any potential sources of bias can also help to reduce the uncertainty in coefficients.

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