Re: linear regression with errors in both variables
- To: mathgroup at smc.vnet.net
- Subject: [mg96285] Re: linear regression with errors in both variables
- From: dh <dh at metrohm.com>
- Date: Wed, 11 Feb 2009 05:17:06 -0500 (EST)
- References: <gmrmga$a1k$1@smc.vnet.net>
Hi Joerg,
a least square procedure minimizes (yregi-yi)^2, where yi is a measured
value and yregi the value of the regression line. In your case you want
to minimize the squares sum of the distance perpendicular to the line.
Let's denote the line by a+b x and assume that the data is in d, the we
get the squares sum by:
res[a_, b_] =
1/(1 + b^2) Plus @@ (((b #[[1]] + a - #[[2]])^2) & /@ d )
we then minimize this expression over a and b:
sol = Minimize[res[a, b], {a, b}]
hope this helps, Daniel
Joerg wrote:
> Hi,
>
> I want to test the hypothesis that my data
> follows a known simple linear relationship,
> y = a + bx. However, I have (known) measurements
> errors in both the y and the x values.
>
> I suppose just a simple linear regression
> does not do here.
>
> Any suggestions how do test this correctly?
>
> Thanks,
>
> joerg
>