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Re: st: RE: From: ipianah nic <[email protected]>
From
ipianah nic <[email protected]>
To
[email protected]
Subject
Re: st: RE: From: ipianah nic <[email protected]>
Date
Fri, 11 Feb 2011 12:08:22 +0000
Nick,
my variables are genome and csf, and suppose I use glm/nl what will be
the model that i can use to extrapolate data so that i get predicted
values for where csf values are missing as below
genome csf
> 1 2020
> 2 1747
> 3 1667
> 4 1608
> 5 1578
> 6 1552
> 7 1540
> 8 1526
> 9 1494
> 10 1484
> 11 1476
> 12 1460
> 13 1455
> 14 1449
> 15 1442
> 16 1426
> 17 1418
> 18 1412
> 19 1390
> 20
> 21
> 22
> 23
> 24
Ipianah
On 2/11/11, Nick Cox <[email protected]> wrote:
> You decide whether it is more appropriate to use -nl-, -regress- after
> transformations, or -glm- with a log link. That's (at least) three to choose
> from. There is no answer free of assumptions about the most likely kind of
> error term.
>
> Also, if this is student homework, you got more help than you deserved. Real
> data that people care about don't arrive as Y and X.
>
> Nick
> [email protected]
>
> ipianah nic <[email protected]>
>
> how do I fit an exponential regression curve and extrapolate the data
> using sample data below;
>
genome csf
> 1 2020
> 2 1747
> 3 1667
> 4 1608
> 5 1578
> 6 1552
> 7 1540
> 8 1526
> 9 1494
> 10 1484
> 11 1476
> 12 1460
> 13 1455
> 14 1449
> 15 1442
> 16 1426
> 17 1418
> 18 1412
> 19 1390
> 20 1389
> 21 1384
> 22 1380
> 23 1380
> 24 1365
>
>
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