Weighted Least Squares Fit for Statistical Analysis of Data

In summary, the speaker has a project for a class and needs help understanding the statistical analysis sheet provided. They are specifically questioning the use of Delta R in the equations provided and are seeking clarification on the steps from equation (3) to equation (4).
  • #1
Logik
31
0

Homework Statement



I have a project for one of my class and I have been given a sheet to do the statistical analyst of my data. I am not convince this sheet is proper and I need someone to look over it it. I don't understand where my Delta R goes...


Homework Equations


[tex]
\chi^2 =-\frac{1}{2} \sum_{i=1}^{N}{\left(\frac{y_i-ax_i}{\sigma_i^2}\right)^2} \\
\frac{\partial \chi^2}{\partial a}[/tex]
[tex] = \sum_{i=1}^{N}{\frac{x_i}{\sigma_i^2}(y_i-ax_i)} = 0 \\
\sum_{i=1}^{N}{\frac{x_i y_i}{\sigma_i^2}}[/tex]
[tex] = a\sum_{i=1}^{N}{\frac{x_i^2}{\sigma_i^2}} \\
a= \sum_{i=1}^{N}{\frac{x_iy_i}{x_i^2}} \\
\sigma_a^2 = \sum_{i=1}^{N}{\frac{x_i^2}{\sigma_i^2}} S^2 \\
S^2 [/tex]
[tex][/tex]
[tex]= \frac{1}{N-1}\sum_{i=1}^{N}{\frac{(y_i-ax_i)^2}{\sigma_i^2}} \\
\sigma_a^2 =[/tex]
[tex] \frac{1}{N-1}\left(\sum_{i=1}^{N}{\frac{x_i^2}{\sigma_i^2}}\right)\left(\sum_{i=1}^{N}{\frac{(y_i-ax_i)^2}{\sigma_i^2}}\right) \\
r_n^2 = \left(n-\frac{1}{2}\right)\lambda R \\
a= \sum_{n=1}^{15}{\frac{r_n^2\left(n-\frac{1}{2}\right)}{(r_n^2)^2}} \\
\sigma_a^2[/tex]
[tex] = \frac{1}{14}\left(\sum_{n=1}^{14}{\frac{(r_n^2)^2}{\sigma_r^2}}\right)\left(\sum_{i=1}^{N}{\frac{(r_n^2-a\left(n-\frac{1}{2}\right))^2}{\sigma_r^2}}\right) \\
\Psi(x) = x^2 \\
\sigma_\Psi^2 = (\frac{\partial}{\partial x}(x^2)\Delta x)^2 [/tex][tex]= 4x^2(\Delta x)^2 \\
\sigma_{r_n^2} = 4r_n^2(\Delta r)^2 \\
\sigma_a^2 = \frac{1}{14}\left(\sum_{n=1}^{14}{\frac{(r_n^2)^2}{4r_n^2}}\right)/\left(\sum_{i=1}^{N}{\frac{(r_n^2-a\left(n-\frac{1}{2}\right))^2}{4r_n^2}}\right)
[/tex]


The Attempt at a Solution



The error on r comes from the fact it's a measurement and we square it. I think Delta R should be there somewhere even though it is constant...
 
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  • #2
pdf

PDF of latex file.
 

Attachments

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  • #3
Logik, do not put the spaces in [ tex ] and [ /tex ].

And do NOT write a LONG formula as a single Tex statement.
It will wind up all on one page.
 
  • #4
Logik - regarding your enclosure, I don't understand the step from equation (3) to equation (4). I also don't know if this is relevant to your question.
 

1. What is Weighted Least Square Fit?

Weighted Least Square Fit is a statistical method used to find the best fit line or curve for a set of data points. It takes into account the variability of the data points and assigns weights to them based on their reliability. This allows for a more accurate and precise fit to the data.

2. How does Weighted Least Square Fit differ from Ordinary Least Squares?

Ordinary Least Squares assumes that all data points have equal variability and therefore assigns equal weights to each point. Weighted Least Square Fit, on the other hand, takes into consideration the varying degrees of reliability of the data points and assigns weights accordingly. This makes Weighted Least Square Fit a more accurate method for fitting data.

3. What is the purpose of using weights in Weighted Least Square Fit?

The purpose of using weights in Weighted Least Square Fit is to give more importance to the data points that have less variability and are more reliable. By assigning higher weights to these points, the fit line or curve will be more influenced by them, resulting in a more accurate fit to the data.

4. How are weights determined in Weighted Least Square Fit?

The weights in Weighted Least Square Fit are typically determined based on the inverse of the variance of each data point. This means that data points with lower variance will have higher weights, while data points with higher variance will have lower weights.

5. When should Weighted Least Square Fit be used?

Weighted Least Square Fit should be used when the data points have varying degrees of reliability and the goal is to find the most accurate fit to the data. This method is particularly useful when dealing with data that has heteroscedasticity, meaning the variability of the data changes as the independent variable changes.

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