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Two data sets and want to do a regression excel y = C(x^n) 
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#1
Dec808, 10:49 PM

P: 75

I have two data sets and want to do a regression so that the equation that relates them is of the form y = C(x^n), where C and n are constants.
How do I do this in Excel? 


#2
Dec908, 10:57 AM

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http://www.tcc.edu/faculty/webpages/...el/expstat.pdf CS 


#3
Dec1008, 08:42 AM

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P: 1,378

Note that the aforementioned worksheet deals with the model
[tex] y = b e^{mx} [/tex] and not to [tex] y = C x^n [/tex] 


#4
Dec1108, 12:20 PM

P: 240

Two data sets and want to do a regression excel y = C(x^n)
Take log on both sides. Then the model will reduce to the common one.



#5
Dec1108, 03:34 PM

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No, it does not. The model given in the reference
[tex] y = b e^{mx} [/tex] reduces to [tex] \ln y = \ln b + mx [/tex] The other model reduces to [tex] \ln y = \ln C + n \ln x [/tex] With the obvious changes in notation the first is a simple linear regression model, in which the original [tex] x [/tex] values can be used. In the second both [tex] y [/tex] and [tex] x [/tex] must have their logarithm calculated. Blindly applying the first approach would miss this: that was my point. Of course, there is the question of why Excel would be used for regression in the first place. 


#6
Dec1108, 03:50 PM

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CS 


#7
Dec1108, 07:21 PM

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P: 1,378

Excel doesn't have a very good reputation with regards to statistics (I haven't used the most recent version much, but since the problems that existed in previous versions were never addressed, I would be surprised if they were fixed this time).
There are other problems, but many are not related to regression. I understand why the temptation to use Excel is so high: immense market penetration  it seems almost every school/workplace has it. It is fantastic for many purposes  I just don't think regression in particular, and statistics in general is one of those purposes. I hope this hasn't sounded too much like an angry rant  I apologize if it has. 


#8
Dec1308, 01:43 PM

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#9
Dec1308, 03:19 PM

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#10
Dec1508, 12:46 PM

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