least-squares-regression

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Nicholas Deosaran
Nicholas Deosaran el 14 de Sept. de 2020
Respondida: KSSV el 14 de Sept. de 2020
I need help understing what to do here,
I know i need to slobe for p1 and p2x but stuck, I have the code below but just stuck.
y(饾懃)=饾憹1+饾憹2饾懃
where
x = 0:0.1:20;
noise =?? % a number, to define
y= 4*x + noise*rand(1,length(x));
Vary the value for noise as 0, 50 and 100 to get three different results for 饾憹1and 饾憹2,
From a linear algebra standpoint, determine the coefficients, 饾憹1and 饾憹2, of the least-squares-regression of a line fit through the data defined above.
thank you

Respuestas (1)

KSSV
KSSV el 14 de Sept. de 2020
Read about polyfit. If you have the data x, y you can fit a line and get p1, p2 using polyfit.
p = polyfit(x,y,1) ;
p1 = p(1) ; p2 = p(2) ;

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