Answer:
1. The sum of the residuals is always close to zero.
2.The coefficient of determination measures how much of the variation in the y-values is explained by the regression line.
3. In the equation of the least-squares regression line, y hat is a predicted value when x is known
Step-by-step explanation:
The least square regression line is a line that makes distance from data points to the regression line to be as minimal as possible. This line is a best fit for the data points. Let's say we have a collection of numbers and a scatter plot, this line is a line that exists and best fits the data.
Therefore, the least square regression line minimizes sum of squared error to be close to zero. R² measures the extent to which variations in the value of y is explained by the regression line. In the equation of this line, y hat is a predicted value when x is known.
