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x=[]
y=[]
for _ in range(1000000):
z=random.random()
x.append(z)
y.append((-3*z-8)/4.0)
for _ in range(1000000):
z=random.random()
x.append(z)
y.append((-4*z-7)/3.0)
I just get error when I submit the code and prints nothing when I run the code. Of course I have populated the x and y lists, tried your random population method too, but nothing. Any thoughts thanks a lot
Day 6: Correlation and Regression Lines #1
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I don't see why this isn't 0.96, I get the angle as 16.26 with a cos of 0.96.
Yeah, actually I don't have a clue of correlation of regression. cosine similarity is not the case perhaps, but...
You're assuming that the data has been centered.
So, this code gives -0.68, which is wrong.
#!/usr/bin/python import random,scipy.stats
x=[] y=[] for _ in range(1000000): z=random.random() x.append(z) y.append((-3*z-8)/4.0) for _ in range(1000000): z=random.random() x.append(z) y.append((-4*z-7)/3.0)
a,b = scipy.stats.pearsonr(x,y) print('%.2f'%(a))
Eh, so, I was able to solve this problem.
When scipy.stats.linregress(x,y)[0] and scipy.stats.linregress(y,x)[0] are both -0.75, answer scipy.stats.pearsonr(x,y)[0].
The problem statement is too difficult to understand...
I just get error when I submit the code and prints nothing when I run the code. Of course I have populated the x and y lists, tried your random population method too, but nothing. Any thoughts thanks a lot
a,b = scipy.stats.pearsonr(x,y) print('%.2f'%(a))
For centered data the correlation coefficient is the cosine of the angle, but for un-centered data, it's the secant minus the tangent. [1]
I get the wrong sign when I do it that way, though.