Least-Squares fit for a line Concept

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SUMMARY

The least-squares fit for a line through a set of points (x_1, y_1) to (x_N, y_N) always intersects the "center of gravity" (x-bar, y-bar) of those points. This conclusion is derived from the equations for coefficients A and B, which are calculated using the sums of the x and y values. Specifically, A and B are defined by the equations involving summations of x, y, and their products, ensuring that the line passes through the average coordinates of the dataset.

PREREQUISITES
  • Understanding of least-squares regression analysis
  • Familiarity with statistical concepts such as mean (average)
  • Knowledge of summation notation and its application
  • Basic algebraic manipulation skills
NEXT STEPS
  • Study the derivation of the least-squares regression line
  • Learn about the significance of the center of gravity in data analysis
  • Explore the application of linear regression in Python using libraries like NumPy and SciPy
  • Investigate the implications of outliers on least-squares fitting
USEFUL FOR

Students in statistics, data analysts, and anyone interested in understanding linear regression and its mathematical foundations.

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Homework Statement



Prove the following useful fact: the least-squares fit for a line through any set of points (x_1,y_1) ...,(x_N, y_N) always passes through the "center of gravity" (x-bar, y-bar) of the points, where the bar denotes the average of the N values concerned. [Hint: you know that A and B satisfy the equation:
AN + B(sigma)x_i = (sigma)y_i ]


Homework Equations



A(sigma)x_i + B(sigma) x_i^2 = (sigma) x_i*y_i

A = [(sigma)x^2(sigma)y - (sigma)x(sigma)xy]/ [N(sigma)x^2 - ((sigma)x)^2]

B = [N(sigma)xy - (sigma)x(sigma)y]/ [N(sigma)x^2 - ((sigma)x)^2]


The Attempt at a Solution



I think the solution has to do with the solved equations for A and B and their relation to dividing by N, the number of values concerned, which eventually yields the "center of gravity," that is (x-bar, y-bar). I'd appreciate any help. Thanks in advance! :smile:
 
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