In matlab, fitline fit-line
[coolcode "linenum="off"]
x = 0:5;
y = [0 20 60 68 77 110];
a = polyfit(x,y,1)
yhat = polyval(a,x);
err = yhat - y
MSE = mean(err.^2)
RMSE = sqrt(MSE)
plot(x,y,'o',x,yhat),title('Linear Regression'),...
xlabel('time, s'),ylabel('Temperature, degrees F'),...
grid,axis([-1,6,-2,120]),legend('measured','estimated',4)[/coolcode]
Running this script:
a =
20.8286 3.7619
err =
3.7619 4.5905 -14.5810 -1.7524 10.0762 -2.0952
MSE =
59.4698
RMSE =
7.7117
Thus, the best linear fit is
ˆy = 20.8286x + 3.7619
and the root mean squared error is 7.71,
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