Re: Regression Analysis
- From: Jimmy <jimmy_keen2003@xxxxxxxx>
- Date: Thu, 28 Jul 2005 13:30:11 -0400
Thank you very much for reply and helping me in regression analysis.
But i need your further help as i am not able to include terms you
suggested i.e E and error function. I went to the fuction REGRESS and
i got idea too but i am still not able to include the terms you
suggested and the concept of MSE.
( b=regress(Y,X) %%% i used this function and got 4 constants but
still not all positive and how to calculate errors?? )
Now i understand that my problem is of MULTIPLE REGRESSION and i read
to solve this problem using back slash function in mathematics
funcion.
I request me to suggest in analysis.
Thanks and Regards
Jimmy
Greg Heath wrote:
>
>
> Jimmy wrote:
>> Hi All,
>>
>> I appreciate for your effort for helping me in regression
> analysis.
>> Still i haven't figured out the problem. Here i am gonna
explain
> my
>> problem in detail.
>>
>> Explaination of Regression Analysis:
>>
>> I have exprimental data of the process that means i have ouput
> values
>> for each combination of input values. let say i have input
> Pi,Qi,Ri &
>> Si and corresponding output to those input values is Yi. where
i
>> suggest the no of trial of experiments. So here i have 10 sets
of
>> data for which i have output values.
>
> 10 sets of data. How many observations in each set?
>
>> NOW, i am trying to fit those data in linear relationship that
> can be
>> represented by below equation.
>>
>> Y=A*P + B*Q + C*R + D*S.........where A,B,C,D are constants
>
> You need to add a constant term and an error term to the fit:
>
> Y = E + A*P + B*Q + C*R + D*S + err
>
> You now have 5 parameters to estimate very accurately.
> Depending on the measurement noise levels you may
> need any where from M =10 to 300 measurements (~1 to 30
> per data set). I would feel uneasy if M < ~100.
>
>> Problem:::: I want to find the POSITIVE value of A,B,C,D that
can
> be
>> fit to all the 10 set of trial.....
>
> The positivity constraint requires much more sophisticated
> software than you will want to deal with at this stage.
> I suggest forgetting about that constraint for a while and
> minimize the mean sqrared error
>
> MSE = (1/M)*SUM(i=1,M){erri^2 }
> = (1/M)*SUM(i=1,M){Yi - E -A*Pi -B*Qi -C*Ri -C*Si)^2 }
>
>> this problem seems reverse application of regression analysis
>
> No. It is precisely regression analysis.
>
>> have output values of 10 sets of trial and want to find
constant
> of
>> equation such that every time it satisfy the equation.
>
> If you exclude the error term, every point will not
> satisfy the equation. Only the MSE will be minimized.
>
>> I would greatly appreciate your suggestion for using MATLAB to
> solve
>> this problem.
>
> I have posed the problem so that it can be put in
> matrix form
>
> Y = Y0 + (X-X0)*B1 + e
>
> where X0 and Y0 indicate mean values that are easily
> estimated.
>
> The MSE solution for B1 can be obtained by using
> function REGRESS, the function PINV or the
> slash solution B1 = (X-X0)\(Y-Y0).
>
> help regress
> help pinv
>
> Hope this helps.
>
> Greg
>
> -----SNIP-----
>
>
.
- References:
- Re: Regression Analysis
- From: Greg Heath
- Re: Regression Analysis
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