Hi everyone!

Currently I have this nonlinear regression problem whereby I need to perform a curve fitting based on one whole chunk of raw data(given below).
The program has to be written in C++ which will give the equation's coefficients at the output.

How can I also minimise the error for the cure fitting process? Could anyone help to give an estimate equation for this fitting as well?

Thanks a million!
Regression

Raw data:
X Y
0.25 2000
0.25 1780
0.2 1600
0.2 1520
0.15 820
0.15 800
0.15 940
0.2 1200
0.2 1100
0.2 1120
0.3 1830
0.4 2000
0.4 2500
0.5 2500
0.5 2800
0.6 3000
0.6 3500
0.7 3600
0.75 3600
0.8 3600
0.9 3600
0.85 3600
0.15 620
0.3 1180
0.35 1280
0.25 1060
0.2 840
0.4 1420
0.45 1500
0.5 1600
0.55 1700
0.6 1860
0.65 2000
0.3 1200
0.3 1280
0.3 1400
0.35 1360
0.35 1480
0.4 1520
0.4 1640
0.4 1380
0.45 1660
0.45 1750
0.5 1720
0.5 1820
0.55 1860
0.55 1960
0.2 800
0.2 820
0.25 920
0.25 940
0.3 1080
0.3 1100
0.35 1200
0.35 1160
0.4 1320
0.45 1420
0.45 1360
0.5 1520
0.55 1650
0.55 1700
0.55 1600
0.6 1800
0.6 1700
0.7 1800
0.7 1860
0.8 1950
0.8 2000
0.15 220
0.15 240
0.15 260
0.15 300
0.15 320
0.15 360
0.15 380
0.15 400
0.15 420
0.15 440
0.2 340
0.2 360
0.2 380
0.2 400
0.2 420
0.2 440
0.2 480
0.25 460
0.25 480
0.25 540
0.25 560
0.25 600
0.25 620
0.25 680
0.25 700
0.3 660
0.3 680
0.3 700
0.3 720
0.3 740
0.3 760
0.3 800
0.3 880
0.3 940
0.35 540
0.35 620
0.35 660
0.35 680
0.35 720
0.35 800
0.35 840
0.35 880
0.35 820
0.35 840
0.35 1040
0.4 600
0.4 620
0.4 640
0.4 680
0.4 800
0.4 820
0.4 840
0.4 860
0.4 900
0.4 920
0.4 1060
0.45 620
0.45 640
0.45 700
0.45 920
0.45 960
0.45 1120
0.5 540
0.5 620
0.5 700
0.5 960
0.5 1100
0.5 1140
0.5 1260
0.55 680
0.55 940
0.55 960
0.55 1000
0.55 1020
0.55 1040
0.55 1100
0.55 1200
0.55 1260
0.55 1280
0.6 800
0.6 840
0.6 860
0.6 880
0.6 940
0.6 960
0.6 1000
0.6 1320
0.7 1080
0.7 1220
0.7 1240
0.7 1260
0.7 1280
0.7 1420
0.7 1440
0.7 1460
0.8 1140
0.8 1160
0.8 1200
0.8 1220
0.8 1240
0.8 1320
0.8 1340
0.8 1360
0.8 1400
0.8 1420
0.8 1580
0.8 1600
0.9 1220
0.9 1340
0.9 1400
0.9 1440
0.9 1460
0.9 1480
0.9 1660
0.9 1720
0.9 1800
0.9 1820
0.9 1840
1 1480
1 1560
1 1580
1 1740
1 1760
1 1800
1 1860
1 1880
1 1960
0.15 100
0.15 80
0.2 100
0.2 100
0.25 150
0.3 150
0.3 200
0.4 250
0.4 280
0.4 200
0.5 300
0.5 350
0.55 350
0.6 400
0.6 450
0.6 500
0.7 550
0.7 600
0.7 500
0.7 400
0.75 550
0.75 500
0.75 600
0.8 650
0.8 700
0.85 750
0.85 820
0.9 800
0.9 900
0.9 700
0.9 650
0.2 150
0.15 60