Re: Linear and logarithmic fit
- To: mathgroup at smc.vnet.net
- Subject: [mg39379] Re: [mg39363] Linear and logarithmic fit
- From: Dr Bob <drbob at bigfoot.com>
- Date: Thu, 13 Feb 2003 04:53:22 -0500 (EST)
- References: <200302120854.DAA14844@smc.vnet.net>
- Reply-to: drbob at bigfoot.com
- Sender: owner-wri-mathgroup at wolfram.com
Use regression and choose the model that minimizes the sum of squared
errors (or some other measure of total error, if you prefer).
Here's a bit of sample data, three possible models, and the sum of squared
error for each:
data = {#, Random[] + 0.3Log@# + 0.2#} & /@ Range[9]
{x, y} = Transpose@data;
Fit[data, {1, Log@q}, q]
error = #.# &[(% /. q -> x) - y]
Fit[data, {1, q}, q]
error = #.# &[(% /. q -> x) - y]
Fit[data, {1, q, Log@q}, q]
error = #.# &[(% /. q -> x) - y]
Bobby
On Wed, 12 Feb 2003 03:54:03 -0500 (EST), jay Johnson <joh_nson at yahoo.com>
wrote:
> Hi everybody,
>
> If I have 9 points in a 2 dimensional space how do I decide if they
> fit better a linear function or a logarithmic function?
>
> Thanks in advance,
>
> Jay
>
>
--
majort at cox-internet.com
Bobby R. Treat
- References:
- Linear and logarithmic fit
- From: joh_nson@yahoo.com (jay Johnson)
- Linear and logarithmic fit