Probability and Statistics Lecture 19

Continued

Probability and Statistics Lecture 18 Prerequisites

• Linear Regression

Least Squares Method

The least squares method is a standard approach in regression analysis to approximate the solution of overdetermined systems by minimizing the sum of the squares of the errors made in the results of every single equation.

$$y=a+bX$$

To find the coefficients (a) and (b), we use the following equations:

$$\sum y=\sum a+\sum bX=na+b\sum X$$ $$\sum xy=a\sum x+b\sum x^2$$

These equations are derived from the condition that the sum of the squared differences between the observed and predicted values is minimized.

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References

Information

• date: 2025.03.04
• time: 15:51

X	5	6	7	8	9
Y	2	4	5	6	8

References

Information

• date: 2025.03.04
• time: 15:51

Continued

Probability and Statistics Lecture 20