How can I add noise to linear graph

I have 100 sample points and I'm using y=mx+c to create 1d line and add gaussian noise using randn the attached graph is the result that I got when I tried the first code and when I try the second code I get straight line I think there is something wrong with my plot but I'm not sure where ?

code1
  sample = 100;
    x = linspace(-5,5);
    y= 1.6 * x +6; 
    dataset = randn(sample,2); 

      figure
      plot(dataset,y) 
      axis([-8 7])

code2

sample = 100;
x = linspace(-5,5);

y= 1.6 * x +6; 

dataset = y*randn(sample,2); 

figure
plot(dataset) 
axis([-8 7]);

this is the result that I want to get

Answer 1

[jgg]

You never added the random noise to your dataset:

sample = 100;
  x = linspace(-5,5);
  y= 1.6 * x +6; 
  r = randn(2,sample); 
    figure
    plot(x,r(1,:)+y) 
    hold on
    plot(x,r(2,:)+y)

The actual outcome value is y + r(i,:) not just y; you also are plotting it incorrectly before.

[lucky_]

Thanks that worked I'm still new to this so I wasn't sure .. is there function to plot fitted line

[jgg]

Yes. Let's code it up a little more clearly since you want to fit something too:

sample = 100;
x = linspace(-5,5);
y= 1.6 * x +6; 
r = randn(2,sample);
y_noise = y + r(1,:); 

X = [ones(1,length(x)); x];
beta = regress(y_noise',X');
y_fitted = beta(1) + x*beta(2);

figure
plot(x,y_noise)
hold on 
plot(x,y_fitted)

This fits a linear regression curve to the noisy data using the regress function with an intercept. The coefficients are in beta.

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