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# Need help with Least Squares fit problem! Not overly difficult I don't believe.

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Grant Piephoff on 8 Oct 2017
Commented: Star Strider on 8 Oct 2017
Hello, I am doing some homework for my computational methods class and need some help. We are covering linear regressions and least fit squares. So the problem I am doing is having me derive by hand the least fit squares for the equation y=a1x+e, meaning determine the slope that results from a straight line with a zero intercept. I did it by hand and got a slope of 0.61436, but was wondering how I would go about checking on matlab if this was correct?
The x and y data are as follows: X: 2 4 6 7 10 11 14 17 20 Y: 4 5 6 5 8 8 6 9 12 I understand basic plotting but am unsure of how to fit the line to it on here. Thanks!
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### Accepted Answer

Star Strider on 8 Oct 2017
Use the MATLAB mldivide,\ (link) function to calculate the least-squares fit:
X = [2 4 6 7 10 11 14 17 20];
Y = [4 5 6 5 8 8 6 9 12];
a1_1 = X(:)\Y(:) % Using ‘mldivide,\’
You may want to check your calculations and derivation, since I get a different result from taking the first (partial) derivative of:
S = sum((Y - a1*X)^2)
with respect to ‘a1’, then setting it to zero and solving for ‘a1’:
a1_2 = sum(X.*Y)/sum(X.^2) % Using Computed Least Squares
My derivation result agrees with the mldivide result.
Plotting:
figure(1)
plot(X, Y, 'pg')
hold on
plot(X, a1_1*X, '-r')
hold off
grid
text(7, 11, sprintf('Y = %.3f\\cdotX', a1_1))
text(7, 13, sprintf('Y = %.3f\\cdotX', a1_2))
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Star Strider on 8 Oct 2017
As always, my pleasure!
I decided to do the derivation as well (by hand, not using the Symbolic Math Toolbox) to be certain I could get the same result.

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### More Answers (1)

regress(Y,X)
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