The least squares error of a candidate measures how well that candidate line fits the data: E(m) = (yı – mx1)² + (y2 – mx2)² + .… ·+ (y7 – mx7)2 %3D Each term in the sum above represents the square of the distance between an actual data point Y; and the y-value of the candidate line at at x;. Why is this error a function of m?

Calculus: Early Transcendentals
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Author:James Stewart
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Chapter1: Functions And Models
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Problem 1RCC: (a) What is a function? What are its domain and range? (b) What is the graph of a function? (c) How...
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If I have a list of data and use the least squares error, why would the function be a function of m
### Understanding the Least Squares Error in Candidate Line Fitting

The least squares error measures how well a candidate line fits the data, represented by the following equation:

\[ 
E(m) = (y_1 - mx_1)^2 + (y_2 - mx_2)^2 + \ldots + (y_7 - mx_7)^2
\]

Each term in the sum above represents the square of the distance between an actual data point \(y_i\) and the y-value of the candidate line at \(x_i\). This method aims to minimize these squared differences to find the best-fit line. 

### Key Question

Why is this error a function of \(m\)?

In the context of line fitting, \(m\) represents the slope of the line. By adjusting \(m\), we attempt to minimize the sum of the squared differences, which in turn generates the line that best fits the given set of data points.
Transcribed Image Text:### Understanding the Least Squares Error in Candidate Line Fitting The least squares error measures how well a candidate line fits the data, represented by the following equation: \[ E(m) = (y_1 - mx_1)^2 + (y_2 - mx_2)^2 + \ldots + (y_7 - mx_7)^2 \] Each term in the sum above represents the square of the distance between an actual data point \(y_i\) and the y-value of the candidate line at \(x_i\). This method aims to minimize these squared differences to find the best-fit line. ### Key Question Why is this error a function of \(m\)? In the context of line fitting, \(m\) represents the slope of the line. By adjusting \(m\), we attempt to minimize the sum of the squared differences, which in turn generates the line that best fits the given set of data points.
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