1. Read in the data.

 1. You have read 3 columns of numbers 
    into Dataplot, variables Field,
    Lab, and Batch.

   1. Plot field versus lab.

   2. Condition plot on batch.

   3. Check batch effect with.
      linear fit plots by batch.

 1. Initial plot indicates that a
    simple linear fit is a good 
    inital model.

 2. Condition plot on batch indicates
    no significant batch effect.

 3. Plots of fit by batch indicate no
    significant batch effect.

   1. Linear fit of field versus lab.
      Plot predicted values with the
      data.

   2. Generate a 6-plot for model
      validation.

   3. Plot the residuals against
      the predictor variable.

 1. The linear fit was carried out.
    Although the inital fit looks good,
    the plot indicates that the residuals
    do not have homogeneous variances.

 2. The 6-plot shows that the model
    assumptions are satisfied except for
    the non-homogeneous variances for
    the residuals.

 3. The detailed residual plot shows
    the non-homogeneous variances
    more clearly.

   1. Plot several common transformations
      of the dependent variable (field).
      data.

   2. Plot several common transformations
      of the predictor variable (lab).

   3. Box-Cox linearity plot.

   4. Linear fit of field versus lab.
      Plot predicted values with the
      data.

   5. Generate a 6-plot for model
      validation.

   6. Plot the residuals against
      the predictor variable.

 1. The plots indicate that a log
    transformation on the dependent
    variable (field)is a good candidate
    model.

 2. The plots indicate that a log
    transformation on the predictor
    variable (lab)is a good candidate
    model.

 3. The Box-Cox linearity plot
    indicates an optimum transform
    value of 0.6, although a log
    transformation should work well.

 4. Carry out the log-log fit.
    The plot of the predicted values
    with the data indicate that
    the residuals should have
    homogeneous variances.

 5. The 6-plot shows that the model
    assumptions are satisfied.

 6. The detailed residual plot shows
    more clearly that the homogeneous
    variances assumption is now
    satisfied.

   1. Fit function to determine appropriate
      weight function.  Determine value for
      c in the power model.

   2. Residuals from fit to determine
      appropriate weight function.

   3. Weighted linear fit of field versus
      lab.  Plot predicted values with
      the data.

   4. Generate a 6-plot for model
      validation.

   5. Plot the residuals against
      the predictor variable.

 1. The fit to determine an appropriate
    weight function indicates that a
    value of c in the range 1.5 to 2.0
    should be reasonable.

 2. The residuals from this fit 
    indicate no major problems.

 3. The weighted fit was carried out.
    The plot of the predicted values
    with the data indicates that we 
    now have homogeneous variances.

 4. The 6-plot shows that the model
    assumptions are satisfied.

 5. The detailed residual plot shows
    the homogeneous variances for the
    residuals more clearly.

   1. Plot predicted values from each
      of the three models with the 
      data.

 1. The transformed and weighted fits
    generate lower predicted values for
    low values of defect size and larger
    predicted values for high values of
    defect size.