Calculate Sum of Square Error
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% Extract Data from Excel sheet to Metrix
Metrix = xlsread('D:\data.xlsx');
% X represent annual franchise fee and Y represent start up cost ($1000) for a pizza franchise
X = Metrix(:,1);
Y = Metrix(:,2);
% as we know regression line eq is ---> y = wx+b where w is slope and b is y-intercept
SUMxy = sum(X.*Y);
SUMx = sum(X);
SUMy = sum(Y);
n = length(X);
SUMx2 = sum(X.*X);
SUMxthen2 = SUMx*SUMx;
slope_W = (((n)*(SUMxy)) - (SUMx*SUMy))/((n*SUMx2)-(SUMxthen2));
YIntercept_B = (SUMy/n)-(slope_W*(SUMx/n));
x=linspace(0,2000);
eq_y = slope_W*x+YIntercept_B;
scatter(X,Y,'*');
hold on;
plot(x,eq_y);
hold off;
as we know SSE = (y-y_bar)^2 .. but i have not a y_bar values how to i extract y_bar values in Matrix ?
4 Comments
Image Analyst
on 28 Oct 2013
Please attach your spreadsheet so we can run your code.
Muhammad
on 28 Oct 2013
Azzi Abdelmalek
on 28 Oct 2013
There is no y_bar in your code
Muhammad
on 28 Oct 2013
Accepted Answer
More Answers (1)
Wayne King
on 28 Oct 2013
The sum of square error in regression is the 2-norm squared of the residuals, so if yhat are your fitted values, and y are the original observations, then
r = y-yhat;
SSE = norm(r,2)^2;
3 Comments
Muhammad
on 28 Oct 2013
Wayne King
on 28 Oct 2013
yhat are your fitted values. You have a regression equation, it's your eq_y the values of that equation are your predicted values
Muhammad
on 28 Oct 2013
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