Which function should I use for generating the weighted least squares fit linear line for a given data?

I have a set of data which I generate using the following code:

x = linspace(-10, 10, 20);
slope = 1.5;
intercept = -1;
noiseAmplitude = 15;
y = slope .* x + intercept + noiseAmplitude * rand(1, length(x));

I want to generate a weighted linear least squares fit regression line for the above data points. I don't have the weights matrix so I can go with using the formula w(i) = 1/variance(i)^2 or any other default formula that a MATLAB function may use for generating the weights matrix. I don't know how to generate this matrix and make sure its diagonal and then fit a regression line in the data, is there a function in MATLAB which can help me achieve this?

Answer 1

[Sindar]

fitlm accepts weights as a vector, but doesn't come with any pre-designed ones

mdl = fitlm(x,y,'Weights',weight);
Ypred = predict(mdl,x);
plot(x,y,'k*',x,Ypred,'r')

determining variance values for your points is a separate question (my answer would be "huh? I don't think that's a sensible quantity," but if you ask clearly, others may have an answer)

[Vihaan Misra]

How should I go on and plot it further? Also, how can I generate the weights matrix using the formula I have mentioned in the question?

[Sindar]

edited to answer

[Sindar]

(if the variance is defined from your underlying distribution, it looks like it should be constant since your noise doesn't depend on x)

[Vihaan Misra]

Thanks a lot!! I have been trying to generate the weights matrix but can't seem to do so, I am very new to MATLAB so any input will be really helpful

[Sindar]

I'd check out the Matlab online trainings (I believe most are currently free), starting with the Onramp (always free).