Select the correct definition of a least squares regression line. A line fitted to data points that minimizes the sum of the squared residuals. A line fitted to data points such that the line goes through the greatest number of points. A line fitted to data points such that the sum of the squared horizontal differences between the line and the data points is minimized. A line fitted to data points that minimizes the absolute value of the vertical deviations between the line and data points. A line fitted to data points where the correlation between the variables is at least 0.5.

A line fitted to data points that minimizes the sum of the squared residuals

Step-by-step explanation:

The principle of least square(LS) consists of determining the values of the unknown parameters that will minimize the sum of squares of errors (or residuals) where errors are defined as the differences between observed values and the corresponding values predicted or estimated by the fitted model equation.

Thus, the correct definition of least square regression line is "A line fitted to data points that minimizes the sum of the squared residuals"

1. 3.4/4

2. 0.8/7

3. 0.5 x 3

4. 0.9 x 6

5. 10.8

6. 2.35

7. 0.567

8. 9.35

9. 0.02

10. 0.06

11. 0.16

12. 0.42

13. 0.63

14. 5.6

15. 8.1

16. 9.9

17. 0.28

18. 0.48

19. 0.27

20. 0.49

21. 0.45

22. 0.72

23. 0.9

24. 0.88

25. 0.75

26. 0.16

27. 0.328

28. 0.495

29. 0.72

30. 1.068