Linear fit between two sets of independent variables

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i have data set like
X Y
1 1.005388889
2 0.624308036
5 0.622771858
7 0.294506747
9 0.187576608
i want to plot linear fit showing equation and R-square value on the graph, as shown in figure plotted by excel.

Answers (2)

Jos (10584)
Jos (10584) on 9 Dec 2016
Here are some commands you may find useful. Please take a look at the documentation
X = [...]
Y = [...]
plot(X, Y, 'bo')
[r,p] = corrcoef(X, Y)
p = polyfit(X, Y, 1)
lsline

Star Strider
Star Strider on 9 Dec 2016
Edited: Star Strider on 9 Dec 2016
It is not as straightforward as it at first seems.
The Code
% X Y
D = [1 1.005388889
2 0.624308036
5 0.622771858
7 0.294506747
9 0.187576608]
X = [D(:,1) ones(size(D(:,1)))]; % Design Matrix
B = X\D(:,2); % Estimate Parameters
Yfit = X*B; % Calcualte Regression Line
Resid = D(:,2) - X*B; % Residuals
Rsq = 1 - sum(Resid.^2)/sum((D(:,2)-mean(D(:,2))).^2); % Calcualte R^2
figure(1)
plot(D(:,1), D(:,2), 'bd', 'MarkerFaceColor','b')
hold on
plot(D(:,1), Yfit)
hold off
axis([0 10 0 1.2])
lbl = sprintf('y = %.3f\\cdotx%+.3f\nR^2 = %.3f', [B' Rsq])
text(6, 0.8,lbl, 'HorizontalAlignment','center')
The Plot
EDIT Typographical error in the ‘lbl’ string. Corrected now.

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