# Matlab using the linear regression and linear fitting to find the relationship between the two functions

Source: Internet
Author: User
Tags sprintf
T=[1999 2000 2001 2002 2003 2004 2005 2006 2007 2008]
c=[399.72 506.97 754.98 989.4 1202.48 1473.29 1525 1717.87 1911.45 2004.25]

Find T expression C.

```Figure
T=[1999 2000 2001 2002 2003 2004 2005 2006 2007 2008];
c=[399.72 506.97 754.98 989.4 1202.48 1473.29 1525 1717.87 1911.45 2004.25];
A=polyfit (t,c,2);
ti=1999:1:2008;
Ci=polyval (A,ti);
Plot (t,c, ' go ', ' markeredgecolor ', ' k ', ' markerfacecolor ', ' g ', ' markersize ', 6);
Xlabel (' year ');
Ylabel (' relevant data ');
Axis ([1999 2008 2100]) Hold on plot (Ti,ci, ' linewidth ', 2, ' Markersize ',) Legend (' Raw data points ', ' fitted Curves ') plot (T,c, ' r. ') sprintf (' Curve equation: c=%0.5g*t^2+ (%0.5g) *t+ (%0.5g) ', a (1), A (2), A (3))% curve equation: c=-5.5245*t^2+ (22324) *t+ ( -2.255e+007)%
This is 2 times linear correlated figure;
T=[1999 2000 2001 2002 2003 2004 2005 2006 2007 2008];
c=[399.72 506.97 754.98 989.4 1202.48 1473.29 1525 1717.87 1911.45 2004.25];
A=polyfit (t,c,1);
ti=1999:1:2008;
Ci=polyval (A,ti);
Plot (t,c, ' go ', ' markeredgecolor ', ' k ', ' markerfacecolor ', ' g ', ' markersize ', 6);
Xlabel (' year ');
Ylabel (' relevant data '); Axis ([1999 2008 2100]) Hold on plot (Ti,ci, ' linewidth ', 2, ' Markersize ',) Legend (' Raw data points ', ' fitted Curves ') plot (T,c, '-R. ') sprintf (' Curve equation: c=+ (%0.5g) *t+ (%0.5g) ', a (1), A (2),)% curve equation: c=+ (187.66) *t+ ( -3.7473e+005)% This is a linear correlation ```

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