Linear best fit through specified y-axis intercept
43 views (last 30 days)
Show older comments
Essentially I have a few data points grouped closely together and they require a linear best fit line. However, the best fit line needs extrapolating to a specific y-axis intercept. I'm not too sure how to approach this. Using a polyfit I get this result:
The y intercept has to be 1.68. So I presume the gradient has to be modified as well as the intercept to get the correct linear best fit line. How would I go about this?
0 Comments
Answers (2)
David Hill
on 13 Nov 2019
f=fit(x,y,'poly1');
disp(f);%will display the linear model including the y-intercept and slope of the fitted line
0 Comments
Star Strider
on 13 Nov 2019
Try this:
x = linspace(0.7, 1.7, 5); % Create Data (Use Your Own ‘x’ & ‘y’)
y = randn(1,5); % Create Data (Use Your Own ‘x’ & ‘y’)
xp = [0 2]; % X-Vector For Plot
B = x(:) \ (y(:)-1.68);
yp = xp(:)*B+1.68;
Bnew = [xp(:) ones(size(xp(:)))] \ (yp(:)); % Calculate New Regression Parameters
figure
plot(x, y, '*')
hold on
plot(xp, yp, '-r')
plot(0, 1.68, '+g')
hold off
grid
xlim([0 2])
text(1, 1.5, sprintf('y = %.2f%+.3f\\cdotx', Bnew(2),Bnew(1)))
The ‘Bnew’ calculation is not absolutely necessary, since it is simply [B; 1.68]. I added it essentially to demonstrate that the new regression on ‘xp’ and ‘yp’ does actually result in the desired parameters.
0 Comments
See Also
Categories
Find more on Regression in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!