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MathGroup Archive 2006

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Re: a mahtematica newbie about FitResiduals question:

  • To: mathgroup at smc.vnet.net
  • Subject: [mg67307] Re: [mg67264] a mahtematica newbie about FitResiduals question:
  • From: Darren Glosemeyer <darreng at wolfram.com>
  • Date: Sat, 17 Jun 2006 04:36:35 -0400 (EDT)
  • Sender: owner-wri-mathgroup at wolfram.com

At 06:30 AM 6/14/2006 -0400, William wrote:
>hi:
>in mathematica ,I am learn the regression,.I see the help browser and
>can't understand the
>  calculate.
>
>
>in the  Statistics`LinearRegression`  example of  mathematica help
>browser
>
>this example please see the picture URL:
>http://lh3.google.com/william.wang/RI_KTKEfABI/AAAAAAAAAAY/02gUyKu1iMk/se.jpg?imgmax=1024
>
>
>what mean the SE? Standard Error?
>
>how to calculate the SE? by given Objserved and Prdicted value?
>
>I can get same result with the example SE result.
>
>from Standard Error formula  that Predicted value minus Observed value
>then squared
>  or by Residual error and Standard Error  relationship formula.
>
>
>thanks.


Single prediction standard errors are a combination of the variation in the 
data and the standard errors of the parameters in the model. The following 
will show a couple of ways to obtain the first standard error in the 
table.  The rest can be obtained by following these steps with the other 
data points.

In[1]:= << Statistics`

In[2]:= data = {{0.055, 90}, {0.091, 97}, {0.138, 107}, {0.167, 124}, {0.182,
                 142}, {0.211, 150}, {0.232, 172}, {0.248, 189}, {0.284, 
209}, {0.351,
                 253}};


For the first way of computing the standard error, we will need the 
estimated variance and the covariance matrix.

In[3]:= {estvar, covmat} = ({EstimatedVariance, CovarianceMatrix} /.
                 Regress[data, {1, x^2}, x,
                   RegressionReport -> {CovarianceMatrix, 
EstimatedVariance}] /.
               MatrixForm -> Identity)

Out[3]= {64.9129, {{17.7381, -247.146}, {-247.146, 5430.96}}}



The coefficients for the model for the first data point are 1 and x^2 where 
x is the first predictor value in data.


In[4]:= coeffs1 = {1, data[[1, 1]]^2}

Out[4]= {1, 0.003025}



The standard error is the following.


In[5]:= Sqrt[estvar + coeffs1.covmat.coeffs1]

Out[5]= 9.01141


For other models, the formula above with the coefficients replaced with the 
appropriate coefficients for the given model will work for any number of 
predictors.  The terms in the Sqrt take into account the variance of the 
data, the variances of the parameter estimates and the covariances between 
parameter estimates.

With two parameters, as in the example, the standard error can be computed 
from the fitted values, the mean response, and the estimated variance as 
follows.


In[6]:= fitted = PredictedResponse /.
             Regress[data, {1, x^2}, x, RegressionReport -> 
{PredictedResponse}];

In[7]:= responsemean = Mean[data[[All, -1]]];

In[8]:= datalen = Length[data];

In[9]:= Sqrt[estvar((datalen + 1)/datalen + (fitted[[1]] - responsemean)^2/
                   Total[(fitted - responsemean)^2])]

Out[9]= 9.01141


With a larger number of fitted parameters the variances and covariances 
would need to be enumerated.  The approach in In[5] takes all of this into 
account within the dot products, so it is easier to use that than to 
explicitly write out all of the variance and covariance terms.


Darren Glosemeyer
Wolfram Research


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