Obtaining Coefficients and Uncertainties for a Least-Squares Parabola

  • Thread starter diegojco
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In summary, the speaker has attempted to find information on the expressions for least-squares parabola coefficients and their uncertainties. They have tried to use the minimum condition for partial derivatives to find the coefficients, but the expressions are complex. They also mention that there is a function in Matlab for getting the coefficients, but not the uncertainties. The speaker is upset because they need to obtain the uncertainties for a lab report. They then provide equations for linear least-squares regression and their uncertainties, followed by mentioning that they are trying to do the same for a parabolic least-squares using logarithms and log paper.
  • #1
diegojco
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I have tried to find some information of the expresions for a least-squares parabola coefficients (including their uncertaintities), then I have tried to do it for myself using the minimum condition for partial derivatives as same as with the least-squares line, but the expressions of coefs are so complex, and then I have no idea to obtain uncertaintities. In Matlab are a function to get the coefficients but not the uncertaintities, and I am upset, since I must get how to obtain uncertaintities, it's fundamental for a lab report on Maxwell's Disc.

Please Help Me!
 
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  • #2
For the linear least-squares regression we can get:

y=ax+b

a=(Σxy-nxmeanymean)/(Σ(x^2)-n(xmean^2))

b=ymean-axmean

and their uncertaintities:

Δa=sqrt((Σ((y-(ax)-b)^2))/(n-2))/sqrt(Σ(x^2)-n(xmean^2))

Δb=sqrt((Σ((y-(ax)-b)^2))/(n-2))*sqrt((1/n)+((xmean^2)/D))

where D=(Σ(x^2)-n(xmean^2)). hence we have that the ecuation is:

y=(a±Δa)x+(b±Δb)

Well I'm triying to do the same for a parabolic least-squares.
 
  • #3
for a parabolic least squares, you need to use logarithms.
/s
 
  • #4
plot you graph on log paper. it should make a stright line.
 

Related to Obtaining Coefficients and Uncertainties for a Least-Squares Parabola

What is a least-squares parabola?

A least-squares parabola is a mathematical model used to approximate a set of data points by finding the best-fitting parabola curve. It minimizes the sum of the squared differences between the actual data points and the predicted values from the parabola equation.

How is a least-squares parabola calculated?

The least-squares parabola is calculated by finding the coefficients of the parabola equation that minimize the sum of squared errors between the actual data points and the predicted values. This is usually done using a mathematical method called linear regression.

What are the applications of least-squares parabola?

Least-squares parabola is commonly used in regression analysis to model data that follows a parabolic trend. It can be applied in various fields such as finance, engineering, and science to make predictions and analyze patterns in data.

What are the advantages of using a least-squares parabola?

Using a least-squares parabola allows for a simple and efficient way to model data that follows a curved trend. It also provides a good balance between accuracy and simplicity, making it a popular choice for data analysis.

What are the limitations of least-squares parabola?

Although least-squares parabola can be a useful tool for approximating data, it may not be the best fit for all types of data sets. It assumes a parabolic trend in the data and may not work well for data with non-linear patterns. Additionally, it can be sensitive to outliers in the data.

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