
#1




*ANSWER* Question 4 linear regression hypothesis
On question 4, I tried to fit each of two sample points through (i) h = ax, and (ii) h = ax+b. I found that hypothesis (i) gave me an average of "a" quite different to any of the answers, but hypothesis (ii) gave me an average of "a" very close to one of the answer options and the average of b is virtually close to 0. If average of b is 0 in (ii), why the average of a are different in (i) and (ii)? Can anyone help me explaining these?

#2




Re: Question 4 linear regression hypothesis
Quote:
Having said that, you should get an answer that matches one of the 5 choices when you fit the model properly.
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#3




Re: Question 4 linear regression hypothesis
Thank professor for the reply. I found my current answer is quite close to a similar post last year (i.e., g_hat = 1.42. I pasted that post below). But I could not access to the suggested link for the discussion (http://book.caltech.edu/bookforum/showthread.php?t=424). Does anyone know what causes this solution?
Thanks. Re: Questions 46 of Hwk 4 (bias and variance) Quote: Originally Posted by gmathew I solved this problem very carefully many times. But I am not getting the answers given in the homework solution for 5 and 6. These are the answers I am getting. I am getting ghat = 1.4272x bias = 0.5413 variance = 0.4725 Is anybody else getting similar answers? Can the intructor please verify the answers given in the homework solution? thanks look this topic: http://book.caltech.edu/bookforum/showthread.php?t=424 there is a good discussion about these problems 
#4




Re: Question 4 linear regression hypothesis
Apparently quite some people got the same wrong answer (g_hat = 1.4) according to this post: http://book.caltech.edu/bookforum/showthread.php?t=430.
I am desperately in need of suggestion here to figure out the reason. My thought on this question is pretty straight forward: generate a two point sample: Xsamples = (rand(2, 1)0.5)*2; yn = sin(Xsamples.*pi); then a is calculated by regress(yn, Xsamples) (to minimize the squared error) Repeat 1000 times and take the average of a (because of uniform distributed input X), that gives me 1.42. Any advice is appreciated. 
#5




Re: *ANSWER* Question 4 linear regression hypothesis
I added *ANSWER* to the thread title since you started discussing specific answers.
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#6




Re: Question 4 linear regression hypothesis
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#7




Re: Question 4 linear regression hypothesis
Quote:
What makes you say that is obviously incorrect though? 
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