Linear fit between two sets of independent variables

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.

Answer 1

[Jos (10584)]

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

Answer 2

[Star Strider]

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.