It will minimise the sum of the squared distances from the points to the line of best fit, measured along the axis of the dependent variable.
4
4
2
It is: 9+16 = 25
The line of best fit is also known as the least square line. It uses a statistical technique to determine the line that fits best through a series of scattered data (plots). Using regression analysis, it finds the line that minimizes the amount of errors (deviations - the sum of vertical distance of data points from the line. The result is a unique line that minimizes the total squared deviations, statistically termed the sum of squared errors.
It will minimise the sum of the squared distances from the points to the line of best fit, measured along the axis of the dependent variable.
You can determine the line of best fit by calculating the regression equation that minimizes the sum of the squared differences between the actual data points and the predicted values on the line. This line helps you make predictions by allowing you to estimate the value of the dependent variable for a given value of the independent variable based on the relationship between the two variables in the data.
When the sum of a number plus 3 is squared, it is 11 more than the sum of the number plus 2 when squared.
25
2
3
4
2
2
4
4