       Re: linear regression with errors in both variables

• To: mathgroup at smc.vnet.net
• Subject: [mg96324] Re: linear regression with errors in both variables
• From: dh <dh at metrohm.com>
• Date: Wed, 11 Feb 2009 05:24:15 -0500 (EST)
• References: <gmrmga\$a1k\$1@smc.vnet.net>

```
Hi Joerg,

this is an addendum to my former message for the case where the x and y

axis have different scales. The following example assumes that the error

in the y axis is 100 times that of the x axis. sx and sy denote the

standard deviations. a anb be define again the line: y==a+b x. res

denotes the weighted sum of squares of distances. The metric is taken

as: {{1/sx^2,0},{0,1/sy^2}}

d = Table[{RandomReal[], 100 RandomReal[]}, {4}];

sx = 1; sy = 100;

res[a_, b_] =

Module[{perp, met = {{1/sx^2, 0}, {0, 1/sy^2}}},

perp = {b sx^2, -sy^2};

perp = perp/Sqrt[perp.met.perp];

Plus @@ (perp.met.(# + {a/b, 0}) & /@ d)^2];

sol = Minimize[res[a, b], {a, b}]

fun[x_] = a + b x /. sol[]

Plot[fun[x], {x, 0, 1}, Epilog -> {Red, Point[d]},

PlotRange -> ({0.95 Min @@ #, 1.05 Max @@ #} & /@ Transpose[d])]

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

>

```

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