anchor.diff:  
                               <h3>More information about the way IMPC uses statistics.</h3>
   
  -                            <h4>IMPC statistics</h4>
  -                            
  -                            <p>
  -                            High-throughput phenotyping generates large volumes of varied data including 
  -                            both categorical and continuous data. Operational and cost constraints can 
  -                            lead to a work-flow that precludes traditional analysis methods. Furthermore, 
  -                            for a high throughput environment, a robust automated statistical pipeline 
  +                           <p>
  +                            High-throughput phenotyping generates large volumes of varied data including
  +                            both categorical and continuous data. Operational and cost constraints can
  +                            lead to a work-flow that precludes traditional analysis methods. Furthermore,
  +                            for a high throughput environment, a robust automated statistical pipeline
                               that alleviates manual intervention is required.</p>
   
                               <p>The IMPC uses a variety of statistical methods for making phenotype calls, including:</p>
   
                               <ul>
  -                                <li><a href="https://en.wikipedia.org/wiki/Fisher%27s_exact_test"><strong>Fisher's Exact test</strong></a> - for categorical data parameters</li>
  -                                <li><a href="https://en.wikipedia.org/wiki/Mixed_model"><strong>Mixed model</strong></a> - for continuous data parameters which include random effects </li>
  -                                <li><a href="https://en.wikipedia.org/wiki/Linear_model"><strong>Linear model</strong></a> - for continuous data parameters when random effects are not significant</li>
  -                                <li><a href="https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U"><strong>Mann-Whitney U Rank sum test</strong></a> - for continuous data parameters when conditions for Mixed model are not appropriate</li>
  +                                <li><a href="https://en.wikipedia.org/wiki/Fisher%27s_exact_test"><strong>Fisher's Exact test</strong></a> - used for categorical data parameters</li>
  +                                <li><a href="https://en.wikipedia.org/wiki/Mixed_model"><strong>Mixed model</strong></a> - used for continuous data parameters which include random effects </li>
  +                                <li><a href="https://en.wikipedia.org/wiki/Linear_model"><strong>Linear model</strong></a> - used for continuous data parameters when random effects are not significant</li>
  +                                <li><a href="https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U"><strong>Mann-Whitney U Rank sum test</strong></a> - used for continuous data parameters when conditions for Mixed model are not appropriate</li>
  +                                <li><a href="https://bioconductor.org/packages/release/bioc/vignettes/PhenStat/inst/doc/PhenStatUsersGuide.pdf"><strong>Reference Range Plus</strong></a> - used for some unidimensional data parameters</li>
                               </ul>
  -                            
  -                            <p>
  -                            The Mixed model and Fisher's Exact methods used have been formalized into an R package called 
  -                            <a href="http://bioconductor.org/packages/release/bioc/html/PhenStat.html">PhenStat</a>.  
  +
  +                            <p>
  +                            The Mixed model (MM), Fisher's Exact (FE), and Reference Range Plue (RR+) methods used have been formalized into an R package called
  +                            <a href="http://bioconductor.org/packages/release/bioc/html/PhenStat.html">PhenStat</a>.
                               See the <a href="http://bioconductor.org/packages/release/bioc/vignettes/PhenStat/inst/doc/PhenStatUsersGuide.pdf">complete PhenStat user's guide</a>.
                               </p>
   
                               <p>
  -                            All analysis frameworks output a statistical significance measure, 
  -                            an effect size measure, model diagnostics (when appropriate), 
  +                            All analysis frameworks output a statistical significance measure,
  +                            an effect size measure, model diagnostics (when appropriate),
                               and <a href="${baseUrl}/documentation/graph-help">graphical visualisation</a>.
                               </p>
  -                            
  -                            <p>
  -                            The PhenStat package provides statistical methods for 
  +
  +                            <p>
  +                            The PhenStat package provides statistical methods for
                               the identification of abnormal phenotypes with an emphasis on high-throughput dataflows.
                               The package contains:</p>
                               <ul>
  -	                            <li>dataset checks and cleaning in preparation for the analysis</li> 
  +	                            <li>dataset checks and cleaning in preparation for the analysis</li>
   	                            <li>2 statistical frameworks for genotype to phenotype identification
   		                            <ul>
   			                            <li>Fisher's Exact test for Categorical data</li>
  -			                            <li>Linear Mixed model for continuous</li>
  +			                            <li>Linear Mixed model for continuous data</li>
  +                                        <li>Reference range plus model for low N continuous data</li>
   		                            </ul>
   	                            </li>
   	                            <li> and additional functions that help to decide the correct method for analysis.</li>
                               </ul>
  -                            
  +
                               <p>
                               The Mixed model framework assumes that base line values of the
  -                            dependent variable are normally distributed but batch (assay date) 
  -                            adds noise and models variables accordingly in 
  -                            order to separate the batch and the genotype. Model 
  +                            dependent variable are normally distributed but batch (assay date)
  +                            adds noise and models variables accordingly in
  +                            order to separate the batch and the genotype. Model
                               optimisation starting with:</p>
   
                               <blockquote>
  
positive.code:                             	<i>NOTE: The MM encoded in PhenStat supports an optional "weight" term.</i>
                              	</small></p>
                              </blockquote>
                              
                              <p>
                              The Mixed model framework is an iterative process to select the
                              best model for the data which considers both the best modelling 
                              approach (Mixed model or general linear regression)
                              and which factors to include in the model.
                              </p>
  
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