1. ## Curve Fitting

Determine the equation of a straight that best fit the points:

Code:
X|  0  |   2 |   4 |  6  |  8  |  10  |
Y|1.12 |5.06 |9.09 |13.12 |17.14 |21.11 |

2. Originally Posted by Apprentice123
Determine the equation of a straight that best fit the points:

Code:
X|  0  |   2 |   4 |  6  |  8  |  10  |
Y|1.12 |5.06 |9.09 |13.12 |17.14 |21.11 |
What method are you expected to use? Linear regression by hand? Technology? etc. Please be more specific about what method you're expected to use.

3. Originally Posted by mr fantastic
What method are you expected to use? Linear regression by hand? Technology? etc. Please be more specific about what method you're expected to use.
Least Squares method
I do not understand is how to find the equation of the line. What would be the polynomial

4. Originally Posted by Apprentice123
Least Squares method
I do not understand is how to find the equation of the line. What would be the polynomial
Please answer the question I asked in my first reply: Are you expected to do it using technology (eg. calculator that has a linear regression program) or by hand?

5. Originally Posted by mr fantastic
Please answer the question I asked in my first reply: Are you expected to do it using technology (eg. calculator that has a linear regression program) or by hand?
Ohh, sorry. Is by hand

6. Then what formulas do you know? I doubt that you are expected to derive the "least squares" solution by from scratch.

7. Originally Posted by HallsofIvy
Then what formulas do you know? I doubt that you are expected to derive the "least squares" solution by from scratch.
I know the method of least squares, where I have several meeting points and a polinimio.
What polynomial I need to find in this case?

8. There are several different formulas to compute "least squares". What formula are you using?

9. Originally Posted by HallsofIvy
There are several different formulas to compute "least squares". What formula are you using?
Is this:

$\sum_{k=1}^{m} (d_k)^2 = \sum_{k=1}^{m} [f(x_k) - \phi (x_k)]^2$

$\phi (x) = \alpha_1 g_1(x) + ... + \alpha_n g_n(x)$

$F(\alpha_1 , \alpha_2 , ..., \alpha_n ) = \sum_{k=1}^{m} (d_k)^2 = \sum_{k=1}^{m} [f(x_k) - \phi (x_k)]^2$