To detect panellists that are possible outliers, highlighting those with extremely high or low values.

- See the profiling dataset.

**Type of Panel Mean:**Adjusted or Arithmetic. Arithmetic means are the well known means, calculated by summing the observations then dividing by the number of observations. Adjusted means, commonly called Least-Squares means (LSmeans) or Estimated Marginal Means (EMMs), use a regression model to calculate the means adjusting for the balance of the data.**Number of standard deviations:**Assessors whose mean falls outside plus or minus this number of standard deviations from the panel mean are highlighted as possible outliers.**Y-axis Scale:**Automatic or Manual. Automatic chooses the plotting range from the range of each attribute. Manual uses the plotting range you specified.**Y-axis min value:**The smallest value that will appear on all plots when specifying the plotting range manually.**Y-axis max value:**The largest value that will appear on all plots when specifying the plotting range manually.**Anonymise Assessors?**Choose to replace the assessor names or not. There are options for randomly generated names or names from the assessor metadata.**Anonymise Products?**Choose to replace the product names or not. There are options for randomly generated names or names from the product metadata.**Anonymise Attributes?**Choose to replace the attribute names or not. There are options for randomly generated names or names from the attribute metadata.

- For each product and attribute a bar plot of
panellist means is displayed. These plots overlay the panel mean as a black
line and the chosen number of standard deviations from this mean as two dotted
lines. Any panellist outside this range is highlighted as a possible outlier.
- If “Type of Panel Mean” was set to “Adjusted”
then the panel means will be adjusted means from models of the form: Attribute
= Product + Assessor + Replicate + Residuals or Attribute = Product + Assessor
+ Residuals if replications are not present.
- Outliers should not necessarily be removed.
- For a large number of draws from a normal distribution it is expected that around 5% of observations would be outside of plus or minus 1.96 standard deviations from the mean.

- R packages: This analysis uses SensoMineR to calculate the adjusted means.

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