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)