Plotting a mountain plot

Plot a mountain plot to see the distribution of the differences between two methods.

1. Select a cell in the dataset.
2. On the Analyse-it ribbon tab, in the Statistical Analyses group, click Method Comparison (or Agreement depending on the edition), and then click Mountain.
   The analysis task pane opens.
3. If the data are in 2 variables:
   1. In the X drop-down list, select the comparative or reference measurement procedure variable.
   2. In the Y drop-down list, select the test measurement procedure variable.
   Note: If the variables consist of replicate measurements, select the variable name that spans all the replicate columns.
4. If the data are in 2 variables with a separate variable identifying replicates of each item:
   1. In the X drop-down list, select the comparative or reference measurement procedure variable.
   2. In the Y drop-down list, select the test measurement procedure variable.
   3. In the Item drop-down list, select the variable identifying each item.
5. If the data are in a single variable with a separate variable matching each item and a variable identifying the method:
   1. In the Model drop-down menu, select Matched Pairs.
   2. In the Y drop-down list, select the measurement variable.
   3. In the Item drop-down list, select the item variable that identifies each item.
   4. In the Method drop-down list, select the method variable.
6. If the data are measured in replicate and the X method is a reference method and the Y method is a comparative method, select the Mean X, 1st Y replicate check box so that the differences represent the difference between an individual test result by the Y method and the average of the replicates for the X method. These differences are the best representation of the total error in the test method, by using the mean of the X method it reduces the amount of random error in the X result so it reflects the true value of the reference method to compare the test method against. If you select Mean X, Mean Y, the differences will only represent the some of the error, namely the systematic error due to bias with some of the random error removed due to averaging of replicates. Likewise, if you select 1st X, replicate, 1st Y replicate the differences will include any random error in the X method so not reflect the error just in the test method. There may be occasions when these other sources of error are of interest, but generally the interest is in the total error of the test method.
7. Click Calculate.