Least square fit (regression analysis)

I have a x and y data, trying to find the the a and b coefficients using least square fit.

this is my code

My r transpose need to be in the format:

% define coordinates
x = [10 20 30 40 50 60 70];
y = [0.765 0.995 1.248 1.524 1.695 2.132 2.463];
plot(x,y,'*');
axis([0 80 0 2]);
hold on
A1 = zeros(1);
for i = 1:7
    A1(i,1) = x(i)^2;
    A1(i,2) = x(i);
    A1(i,3) = 1.0;
end
r = inv(A1'*A1)*(A1'*y);
t = (0:0.1:7);
y1 = r(1)*t.^2 + r(2)*t + r(3);
ploy(t,y1,'r');

Error message
Error using  * 
Incorrect dimensions for matrix multiplication. Check that the number of columns in
the first matrix matches the number of rows in the second matrix. To perform
elementwise multiplication, use '.*'.

I did this: inv(A1'*A1)*A1'*y

But got an error message

Any possible fix is highly appreciated. Thanks

Answer 1

[Bruno Luong]

The y vector must be column

r = inv(A1'*A1)*(A1'*y(:));