1. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Animations are used to show ‘Challenge’,’Syngenta’ and ‘Experiment’ boxes consecutively.
Animation for sprayer picture to transition left to right, then for the pictures/text to display consecutively from left to right, and down the paragraph.
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment with the use of JSL.
Experiment
To determine the uncertainty in % AI remaining after rainfall, the variability in both the rain and no rain samples must be taken into account
This is calculated using formula columns as it can’t be automatically calculated through JMP platforms
Plants sprayed with agrochemical product
Syngenta
Global Swiss agribusiness whose purpose is bringing plant potential to life.
Rainfastness
Agrochemical products are generally sprayed onto leaves of crops/weeds.
In heavy rainfall, some of the product may be washed off the leaf surface, resulting in a loss of activity.
Rainfastness of products can be assessed in the lab by applying the product to leaves followed by simulated rain and determining the % product remaining after rainfall.
Second half of plants subjected to rain then washed and amount of product on leaves quantified – rain value
One half of the plants washed ~ 2 hours after spraying and amount of product on leaves quantified – no rain value
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath
95% 𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙
= M ± 2 ×𝑀× 𝑆𝑡𝑑 𝐷𝑒𝑣 𝑟𝑎𝑖𝑛 𝑁 𝑟𝑎𝑖𝑛 𝑀𝑒𝑎𝑛 𝑟𝑎𝑖𝑛 𝑆𝑡𝑑 𝐷𝑒𝑣 𝑛𝑜 𝑟𝑎𝑖𝑛 𝑁 𝑛𝑜 𝑟𝑎𝑖𝑛 𝑀𝑒𝑎𝑛 𝑛𝑜 𝑟𝑎𝑖𝑛 2
𝑀𝑒𝑎𝑛 % 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑅𝑒𝑚𝑎𝑖𝑛𝑖𝑛𝑔
𝐴𝑓𝑡𝑒𝑟 𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙 (M)
= 100 × 𝑀𝑒𝑎𝑛 𝑟𝑎𝑖𝑛 𝑀𝑒𝑎𝑛 𝑛𝑜 𝑟𝑎𝑖𝑛
% 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 𝑟𝑒𝑚𝑎𝑖𝑛𝑖𝑛𝑔 𝑎𝑓𝑡𝑒𝑟 𝑟𝑎𝑖𝑛𝑓𝑎𝑙𝑙=100× 𝑟𝑎𝑖𝑛 𝑣𝑎𝑙𝑢𝑒 𝑛𝑜 𝑟𝑎𝑖𝑛 𝑣𝑎𝑙𝑢𝑒

2. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Animation to display ‘Solution’ box upon clicking.
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
The previous common processes were:
to rely on the help of a statistician , or experienced person, to analyse and interpret the data correctly.
to ignore the variance associated with the rainfastness of each treatment and simply compare the means, resulting in incorrect conclusions.
to ignore the variance associated with the ‘no rain’ samples by dividing each ‘rain’ value by the average of the ‘no rain’ values. This resulted in smaller than actual confidence intervals in the rainfastness of each treatment therefore incorrect conclusions.
Solution
Build a template data table with a series of scripts to visualise the data, compare rainfastness of treatments to a control via a Dunnett’s test.
The user inputs raw data and treatment details.
The one click scripts allow the user to:
Check that the data has been inputted correctly.
Look for chemical degradation/carryover during the analytical run.
Check that the data satisfies the criteria to perform a Dunnett’s test (equal variance).
Display the results of a Dunnett’s test with a simple graphic that demonstrates which treatments have significantly different rainfastness to a control.
Display a plot of rainfastness Vs. treatments with pooled confidence intervals shown.
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath

3. Formula columns pull sample information out of the sample name
Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Animation to display ‘arrow and data table’ box upon clicking.
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
Step 1: Paste in raw data
Paste sample name, analytical run order, and sample concentration data into the corresponding columns in the table.
Formula columns pull sample information out of the sample name
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath

4. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
Step 2: Check that the data has been imported correctly
Run a script that plots a boxplot for each treatment/time point combination
Check the number of data points, y values (active ingredient concentrations) for rain Vs. no rain
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath

5. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
Step 2: Check that the data has been imported correctly
Run a script that plots a boxplot for each treatment/time point combination
Check the number of data points, y values (active ingredient concentrations) for rain Vs. no rain
Acknowledgements:
Niall Thomson,, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath

6. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
Step 3: Check for patterns with the analytical run order
Run a script that plots [AI] (proportional to product amount) Vs. analytical run order for each treatment/time point combination
Mostly positive correlations – indicative of carryover
Mostly negative correlations – indicative of chemical degradation over the analytical run
In this case, there is no evidence of carryover/degradation over the run as there are a mixture of positive and negative slopes and none of the slopes are significant.
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath

7. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
Step 4: Fill Out Control Treatment and Groups for Dunnett’s Analysis
Under the ‘Control Treatment’ column, set the value for the control treatment, e.g. product with no additive, as yes. For other treatments, set the value as no.
Under the ‘Control Group’ column, set the value of all ‘no rain’ samples as yes, and the ‘rain’ samples as no
Under the ‘Compare to Control’ column, set the value of all ‘rain’ samples as yes, and the ‘no rain’ samples as no
For % product remaining after rainfall
Control group = No rain samples
Compare to control = rain samples
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath

8. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
Step 4: Fill Out Control Treatment and Groups for Dunnett’s Analysis
Defining the control treatments and groups allows the rainfastness (% product remaining after rainfall) and confidence intervals for each treatment to be calculated, along with a Dunnett’s test that tests the significance of the rainfastness of each treatment, compared to the control.
The Dunnett’s test is a multiple comparison test to compare a number of treatments individually to a control. This is analogous to multiple t tests, restricting the number of comparisons to those including the control while maintaining an overall type 1 error rate (α* = α/K (0.05/number of comparisons), standard deviation = a pooled estimate across the treatments).
The test is performed via formula columns. It is not possible to use the fit model platform for this as the rainfastness is calculated using the rain and no rain values, so the variability associated with both of these values has to be taken into account to manually generate the confidence intervals and pooled standard deviation.
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath
95% 𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙
= M ± 2 ×𝑀× 𝑆𝑡𝑑 𝐷𝑒𝑣 𝑟𝑎𝑖𝑛 𝑁 𝑟𝑎𝑖𝑛 𝑀𝑒𝑎𝑛 𝑟𝑎𝑖𝑛 𝑆𝑡𝑑 𝐷𝑒𝑣 𝑛𝑜 𝑟𝑎𝑖𝑛 𝑁 𝑛𝑜 𝑟𝑎𝑖𝑛 𝑀𝑒𝑎𝑛 𝑛𝑜 𝑟𝑎𝑖𝑛 2
𝑀𝑒𝑎𝑛 % 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑅𝑒𝑚𝑎𝑖𝑛𝑖𝑛𝑔
𝐴𝑓𝑡𝑒𝑟 𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙 (M)
= 100 × 𝑀𝑒𝑎𝑛 𝑟𝑎𝑖𝑛 𝑀𝑒𝑎𝑛 𝑛𝑜 𝑟𝑎𝑖𝑛

9. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
Step 5: Check that the data meets Dunnett’s test requirements
Run a script to check the heterogeneity of variance across treatments/timepoints
One of the assumptions of the Dunnett’s test is that the variance across treatments is equal.
If the standard deviation of each treatment/timepoint combination is not considered significantly different to the mean standard deviation (i.e. they lie within the shaded region of the graph) then the Dunnett’s test is valid.
In this case the standard deviation in product amount for each treatment/timepoint combination is not significantly different to the average standard deviation so the Dunnett’s test is valid.
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath

10. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
Step 6: Determine if the rainfastness of treatments are significantly different to the control
Run a script that plots rainfastness Vs. treatment.
The treatments with red points, above the critical value, have significantly different rainfastness to the control (Dunnett’s test, α=0.05). The treatments with green points, within the critical range, are not found to have significant different rainfastness to the control in this test.
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath

11. Surviving the Rain: The Six-Step Programme for a JMP® Novice Stephanie Lucas, Senior Formulation Chemist, Syngenta,
Stephen Pearson, Chemical Process Statistician, Syngenta,
Alan Brown, Statistician, Syngenta,
Challenge
Allow non-statisticians and JMP® novices to analyse data objectively from an agrochemical rainfastness experiment.
Step 6: Rainfastness Vs. Treatment Plot
Acknowledgements:
Niall Thomson, Dave Bartlett, Karen Meade, Anne Stalker, Mark Brittin, David Lomath