I am OK with three different x and y values ... I'm not sure why that
never occurred to me!

Would it help to send you ALL of the data together?

On Nov 26, 9:22 am, Dilip Pandey <dilipan...@gmail.com> wrote:
> Hi Bob,
>
> Ok, in order to determine the best Singular X and Y values we need to break
> down or segregate the large data into the similar type of data sets.  After
> this segregation we can have 2 or 3 sets of Singular Xs and Ys which can be
> used to fulfill / justify the equation as one set of X and Y can satisfy one
> set (segregation) of data set.
>
> Let me know if you are OK with having three different Xs and Ys for this
> kind of huge data because as per my observation, after seeing your data and
> trends, it is very difficult to have a Singular X and Y to justify the
> equation.
>
> Best Regards,
> DILIPandey
>
>
>
>
>
>
>
>
>
> On Fri, Nov 26, 2010 at 9:23 AM, Bob <bobwo...@gmail.com> wrote:
> > While the Goal Seek feature is interesting and I thank you for
> > bringing it up, I don't think it answers my problem.
>
> > I'm attempting to make a predictive model, so I'm looking for a method
> > to determine the best X and Y value to fit the data set. So, while X
> > and Y certainly change from record to record, I want to determine the
> > best singular X and Y values to fit the data set at large.
>
> > So, the answer to your question of how I'm determining the X and Y is
> > that I don't know! That's why I'm posting here -- to come up with a
> > method of determining a proper X and Y in a non-linear equation.
>
> > Thanks again for your help!
>
> > On Nov 24, 10:35 pm, Dilip Pandey <dilipan...@gmail.com> wrote:
> > > Hi Bob,
>
> > > I have done something in the attached file.
> > > I believe if the x and y are not fixed at all the time, then we can
> > > use Goal Seek feature of Excel to obtain the desired results.
> > > Question:- How are you determining X and Y when you already have As and
> > Bs.
>
> > > Best Regards,
> > > DILIPandey
>
> >  > On 11/23/10, Bob <bobwo...@gmail.com> wrote:
>
> > > > Hello,
>
> > > > Thanks for your response!
>
> > > > Here's a random sampling of the data:
>
> > > > 169        140     10
> > > > 164        197     19
> > > > 50 339     31
> > > > 73 326     19
> > > > 204        238     34
> > > > 143        201     18
> > > > 59 74      3
> > > > 296        216     20
> > > > 31 121     9
> > > > 108        148     7
> > > > 139        317     24
> > > > 41 263     14
> > > > 233        286     35
> > > > 221        121     17
> > > > 111        304     25
> > > > 39 325     13
> > > > 201        211     37
> > > > 474        102     72
> > > > 28 158     7
> > > > 268        230     34
> > > > 246        202     19
> > > > 199        208     50
> > > > 144        447     48
> > > > 358        138     61
> > > > 300        8       27
> > > > 301        255     37
> > > > 197        169     24
> > > > 236        205     54
> > > > 28 329     14
> > > > 150        159     23
> > > > 244        298     52
> > > > 74 108     6
> > > > 202        159     37
> > > > 259        127     23
> > > > 19 350     16
> > > > 189        131     29
> > > > 40 183     17
> > > > 369        255     62
> > > > 122        236     3
> > > > 310        201     51
> > > > 95 162     10
> > > > 335        162     37
> > > > 91 447     55
> > > > 171        179     21
> > > > 110        215     9
> > > > 87 242     17
> > > > 328        219     49
> > > > 225        182     21
> > > > 245        243     42
>
> > > > The data in the first two columns (A and B for purposes explained
> > > > below) are the independent variables, with the third column being the
> > > > dependent (C)
>
> > > > Currently, the equation I'm using is:
>
> > > > -10.5348929531027+0.113896884380505*A+0.0893951738657916*B = C
>
> > > > As mentioned, this does not fit the data particularly well. It
> > > > predicts too low a result when A and B are small, and too high a
> > > > result when A and B are large. For this reason, I believe a
> > > > logarithmic curve will best represent the data ... but other
> > > > suggestions are also certainly welcome.
>
> > > > Hope you, or someone, can help! Thank you.
>
> > > > On Nov 23, 9:13 am, Dilip Pandey <dilipan...@gmail.com> wrote:
> > > >> Hi Bob,
>
> > > >> This seems to be interesting to me.  Would you mind providing some
> > > >> sample data with the the indicative result which you want to obtain
> > > >> through Excel.
>
> > > >> Best Regards,
> > > >> DILIPandey
>
> > > >> On 11/23/10, Bob <bobwo...@gmail.com> wrote:
>
> > > >> > Good afternoon,
>
> > > >> > I have a fairly large data set (roughly 7,000 samples) on which I
> > > >> > would like to run some nonlinear regression analysis.
>
> > > >> > I'm fully capable of using the Data Analysis Toolpak and running the
> > > >> > linear analysis there, but unless I'm totally missing it, there
> > > >> > doesn't seem to be a method for running nonlinear analysis.
>
> > > >> > To be clear, what I want to do is take this data, which is in three
> > > >> > columns, and essentially put together an equation to represent Ax +
> > > >> > By
> > > >> > = C.
>
> > > >> > However, this data does not seem to be all that well represented by
> > a
> > > >> > linear equation, and I believe it would be more precise to use an
> > > >> > exponential or logarithmic model along the lines of A^x + B^y = C.
>
> > > >> > Can anyone help?
>
> > > >> > Thank you!
>
> > > >> > --
>
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> > > >> DILIP KUMAR PANDEY
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> > > --
> > > Thanks & Regards,
>
> > > DILIP KUMAR PANDEY
> > >   MBA-HR,B.Com(Hons),BCA
> > > Mobile: +91 9810929744
> > > dilipan...@gmail.com
> > > dilipan...@yahoo.com
> > > New Delhi - 62, India
>
> > >  Analysis.xls
> > > 22KViewDownload
>
> > --
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> --
> Thanks & Regards,
>
> DILIP KUMAR PANDEY
>   MBA-HR,B.Com(Hons),BCA
> Mobile: +91 9810929744
> dilipan...@gmail.com
> dilipan...@yahoo.com
> New Delhi - 62, India

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