Weighted average of arbitrary k points from a line

In summary, the weighted barycenter (x_o) satisfies the equation x_o = a*x_i+b*y_iwhere a and b are arbitrary weights and x_i and y_i are the x and y coordinates of the point x_o.
  • #1
onako
86
0
Suppose a set of k arbitrary points, x_i, 1<=i<=k, x_i from R^2 are selected from a line. How can it be shown that a weighted barycenter x_o=(o_i*x_i)/(o_1+o_2+...+o_k) also belongs to that line (assume o_i are arbitrary weights)? Does the choice of weights restrict the solutions (ie, a particular choice to satisfy that x_o is within the 'convex hull' of other points)?
 
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  • #2
Hey onako.

If this is a straight line then from this you have n-independent coeffecients (since the o_i's are independent) and since you can specify those then you can choose the coeffecients such that these match the ones of the original line.

If all the x_i's line on a straight-line, then provided that you can pick the o_i's, then you can solve for the particular values of o_i to provide equal coeffecients.

The only thing though about this is that you are dividing by the sum of the all the o_i's which means you lose a degree of freedom and the only way you can get a solution is if you have an extra constraint on the o_i's and this constraint is exactly what is imposed.

If your line is a unique line then the solution in terms of your o_i's should be unique as well since you have a constraint on the sum of the weights which means the scalar multiples should disappear as well (I'm consider scalar multiples of a linear equation).
 
  • #3
Let's simplify the question. Suppose that non-negative arbitrary weights o_i are associated with each x_i chosen from the straight line. Does x_o lie on that same line? The point is: points x_i are chosen as arbitrary points from the line, and are associated coefficients o_i which are non-negative.
 
  • #4
Are you looking at the triangle simplex in R^2?
 
  • #5
I'm concerned with a 2D case, with a straight line.
 
  • #6
If this line is in R^2 then they need a common gradient: try setting up a simple line based on y - y0 = m(x - x0) where m = (y1 - y0)/(x1 - x0) and showing that the gradient has a common form.

For the bary-centric case the sum of all the weights should be 1 so you can cancel that out, and see if calculating this line gives a common form in terms of the coeffecients.
 
  • #7
A line in the plane satisfies a vector equation of the form
[itex] Nx=a [/itex]
So if each xi satisfies that equation, you just have to show that a weighted average of them also satisfies that equation. As long as the weights add up to 1 it should work.
 

Related to Weighted average of arbitrary k points from a line

1. What is the formula for calculating the weighted average of arbitrary k points from a line?

The formula for calculating the weighted average of arbitrary k points from a line is:

WA = ((w1 * x1) + (w2 * x2) + ... + (wk * xk)) / (w1 + w2 + ... + wk)

Where WA is the weighted average, w is the weight of each point, and x is the numerical value of each point.

2. How is the weight of each point determined in the weighted average formula?

The weight of each point is typically determined based on the significance or importance of that point in the overall data set. Points that are more relevant or have a higher impact on the data are assigned a higher weight, while less significant points are assigned a lower weight. In some cases, the weight may also be determined by the distance of the point from the mean or median of the data set.

3. Can the weighted average of arbitrary k points from a line be used to determine a trend in the data?

Yes, the weighted average of arbitrary k points from a line can be used to determine a trend in the data. By calculating the weighted average at different points along the line, one can see if the trend is increasing, decreasing, or remaining relatively constant. This can be useful in analyzing data and making predictions.

4. What is the purpose of using a weighted average instead of a regular average?

The purpose of using a weighted average is to give more weight or importance to certain data points that are more significant or relevant. This can help to provide a more accurate representation of the data, especially if there are outliers or extreme values that could skew the regular average.

5. Are there any limitations to using the weighted average of arbitrary k points from a line?

One limitation of using the weighted average of arbitrary k points from a line is that it relies on the accuracy and relevance of the data points being used. If there are any errors or outliers in the data, it can affect the accuracy of the weighted average. Additionally, the formula may not be suitable for all types of data sets and may not provide an accurate representation in certain situations.

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