What is the best estimate for B in Least Squares Fitting?

In summary, the best estimate for B can be found using the formula B= [Sum(xy)]/[Sum(x^2)] by plugging in the values for N, Sum(x), Sum(y), and Sum(x^2). The expression can be simplified to [Sum(x)Sum(y)] * [(N - 1)] / [Sum(x^2)] [N - Sum(x^2)], but it is unclear what to do with the terms N - 1 and N - Sum(x^2). It is possible that when N = 0, at the origin, the expression simplifies to just the best estimate for B.
  • #1
nickerst
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0
1. Homework Statement

Suppose two variables x and y are known to satisfy a relation y=Bx. That is a graph of x vs. y is a line through the origin. Suppose further that you have N measurements (xi,yi)and that the uncertainties in x are negligible and those in y are equal. Prove the best estimate for B is B= [Sum(xy)]/[Sum(x^2)]

2. Homework Equations

B= [(N Sum(xy))-(Sum(x))*(Sum(y))]/[Del]

Del = [N(Sum(x^2))] - (Sum(x))^2]

3. The Attempt at a Solution [/b]

So I plugged the equation of Del into the equation for B so I can try to simplify it and therefor show the best estimate. But it just gets more and more complicated. Is that for sure where I should start?
 
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  • #2
I simplified the expression for B into...

[Sum(x)Sum(y)] * [(N - 1)] / [Sum(x^2)] [N - Sum(x^2)]

This almost gives me what I want but I'm not sure what to do with the N - 1 and N - Sum(x^2). Might it be that when N = 0 (at the origin) it reduces the expression to just the best estimate for B?
 

What is the purpose of Least Squares Fitting?

The purpose of Least Squares Fitting is to find the best-fit line or curve that represents the relationship between two or more variables in a data set. It is used to minimize the sum of the squared differences between the actual data points and the predicted values from the fit.

What is the difference between linear and nonlinear Least Squares Fitting?

Linear Least Squares Fitting is used when the relationship between the variables can be described by a straight line, while nonlinear Least Squares Fitting is used when the relationship is more complex and cannot be represented by a straight line. Nonlinear Least Squares Fitting involves fitting a curve or surface to the data points.

How is the best-fit line or curve determined in Least Squares Fitting?

The best-fit line or curve is determined by minimizing the sum of the squared differences between the actual data points and the predicted values from the fit. This is done by adjusting the parameters of the line or curve until the sum of the squared differences is as small as possible.

What are the assumptions made in Least Squares Fitting?

The main assumptions made in Least Squares Fitting are that the relationship between the variables is linear or can be approximated by a linear function, and that the errors in the data are normally distributed with a mean of zero and constant variance. Additionally, the data points should be independent of each other.

What are the applications of Least Squares Fitting?

Least Squares Fitting is used in various fields, such as statistics, engineering, and physics. It is commonly used for data analysis, prediction and forecasting, and parameter estimation. It is also used in regression analysis and curve fitting to model relationships between variables.

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