a)
Linear equation and data points: Answer D.
b)
For Line A:
X | Y | Predicted value, ŷ | Residual, e | e² |
1 | 4 | -1 + (3) * 1 = 2 | 2 | 4 |
2 | 4 | -1 + (3) * 2 = 5 | -1 | 1 |
3 | 8 | -1 + (3) * 3 = 8 | 0 | 0 |
For Line B:
X | Y | Predicted value, ŷ | Residual, e | e² |
1 | 4 | 1 + (2) * 1 = 3 | 1 | 1 |
2 | 4 | 1 + (2) * 2 = 5 | -1 | 1 |
3 | 8 | 1 + (2) * 3 = 7 | 1 | 1 |
c)
Line B is the better fit because it has smaller sum of squared errors.
Given to the right are two linear equations and a set of data points a. Graph the linear equations and data points. b. Complete tables for x, y, ye, and e. c. Determine which line fits the set of data points better according to the least squares criterion 2 1D b. Complete the table for x,y.9., and e for Line A Line Ay= -1 + 4x X Y (Simplilly your answers.) Enter your answer in the edit fields and then...
Given to the right are two linear equations and a set of data points a. Graph the linear equations and data points b. Complete tables for x, y. y. e, and e c. Determine which ine fits the set of data points better according to the least-squares crterion Line A y 1+2 Line B y-2+3x 10- b. Complete the table for x. y y. e and e for Line A Line A y 1+2x e" y 2 8 (Simpify your...
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Bonus question 7. Suppose that a scientist has reason to believe that two quantities and y are related linearity, that is, y = mc + b, at least approximately, for some values of m and b. The scientist performs an experiment and collects data in the form of points (1,1), (22, y2),..., nyn), and then plots these points. The points don't lie exactly on a straight line, so the scientist wants to find the constants m and b so that...
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Fitting a Line to Data The method of least squares is a standard approach to the approximate solution of overdeter- mined systems, i.e., sets of equations in which there are more equations than unknowns. The term "least squares" means that the overall solution minimizes the sum of the squares of the errors made in the results of every single equation. In this worksheet you will derive the general for- mula for the slope and y-intercept of a least squares line....
Example 1: Least Squares Fit to a Data Set by a Linear Function. Compute the coefficients of the best linear least-squares fit to the following data. x2.4 3.6 3.64 4.7 5.3 y| 33.8 34.7 35.5 36.0 37.5 38.1 Plot both the linear function and the data points on the same axis system Solution We can solve the problem with the following MATLAB commands x[2.4;3.6; 3.6;4.1;4.7;5.3]; y-L33.8;34.7;35.5;36.0;37.5;38.1 X [ones ( size (x)),x); % build the matrix X for linear model %...
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