excel linear regression trouble vs excel linear regression

I am trying to find the equation for the linear regression line of x and y.

I was able to get the linear regression from excel, but I am trying to find it with matlab.

Excel says the linear regression equation is y = -0.003x + 1.7919.

x = 5.92 22.75 73.26 227.56 308.74 589.54 613.66

y = 2.550865 1.869146 1.1623 0.567358 0.459001 0.248734 0.225807

beta = regress(y',x')

% I had to rotate the arrays otherwise the function would not give an answer

the function spits out beta = 7.9666e-04.

Answer 1

[John D'Errico]

Simple enough.

polyfit(x,y,1)
ans =
    -0.002965       1.7919

Or:

regress(y',[x',ones(length(x),1)])
ans =
    -0.002965
       1.7919

Or:

[x',ones(length(x),1)]\y'
ans =
    -0.002965
       1.7919

I could go on for at least a half dozen others. Don't dare me. ;-) With the curve fitting toolbox...

fit(x',y','poly1')
ans = 
     Linear model Poly1:
     ans(x) = p1*x + p2
     Coefficients (with 95% confidence bounds):
       p1 =   -0.002965  (-0.005117, -0.0008127)
       p2 =       1.792  (1.03, 2.554)

>> lsqr([x',ones(length(x),1)],y')
lsqr converged at iteration 2 to a solution with relative residual 0.34.
ans =
    -0.002965
       1.7919
       
>> lsqlin([x',ones(length(x),1)],y')
ans =
    -0.002965
       1.7919
       
>> lsqminnorm([x',ones(length(x),1)],y')
ans =
    -0.002965
       1.7919
       
>> pinv([x',ones(length(x),1)])*y'
ans =
    -0.002965
       1.7919
       
>> lscov([x',ones(length(x),1)],y')
ans =
    -0.002965
       1.7919

Wanna bet I can't come up with a half dozen more? Save your money. :)