I have a table of data that looks like this:
| Target | R1 | R2 | R3 |
|---|---|---|---|
| 3.70 | 0.7863000000 | 0.8313000000 | 1.1019000000 |
| 3.40 | 0.8750000000 | 0.9000000000 | 0.9994000000 |
| 3.40 | 0.7719000000 | 0.9500000000 | 0.9994000000 |
| 3.40 | 0.8550000000 | 0.9019000000 | 1.0763000000 |
| 2.15 | 1.1019000000 | 0.8094000000 | 1.0969000000 |
The real table has about 4,000 values.
I would like to estimate Target based on the R* values.
There should be some algorithm that would come up with a "Best Fit" based on my samples such that I can estimate Target then I insert new values (R1, R2, R3) into the table.
I would suspect it would be something like this:
Target = C1*R1 + C2*R2 + C3*R3 + D1*R1*R1 + D2*R2*R2 + D3*R3*R3
That is, some algorithm will give me C1, C2, C3, D1, D2, D3 that will estimate future values.