Hi @P_Bartell,     Thanks for the links, those are very interesting reads. The statapults are a bit pricey, but look very cool. From the manual, the design seems pretty simple, and one that I might be able to put together with him -- might be a good Scout project for him and I to work on. In the meantime, I think the flying helicopter will be a great example, and he'll have a fun time with it.   Thanks again, everyone, for the feedback in information!   DS ... View more

Hi @markbailey, @P_Bartell, @statman,     I'm happy to report that we have conducted the experiment, and it was a lot of fun! I printed out the runs and my wife had a great time conducting the experiment for us -- she managed the candies, water, coffee grounds, and recording our responses. We both stayed blindfolded the whole time, although could hear each other's responses. I'm attaching the data table, but also just commenting on some of the initial results. As a reminder, there were three factors: the actual candy color (blue, green, pink, purple), tester (A and B), and cleaning method (water swish or smelling coffee grounds)   Overall: the DOE selected each color and cleaning method equally, however shifted one run to one tester over the other, so the testers were not equally chosen. As a collective, the two testers were correct only 28% of the time, not much different than the 25% random chance (no surprise there). Pink was the least frequently predicted color. In fact in the overall heatmap, it's seen that pink was also actually NEVER correctly chosen. If there is a flavor difference, it was one that both testers mixed up with other colors. Contingency analysis results in a likelihood ratio of only 8.4, so pretty small (again, not surprising). Both testers had an equal number of correct answers (4), although unequal number of runs. Overall heatmap shows that blue was correctly chosen the most of all the colors (4 times) with green and purple being equally chosen correctly (2 times each).   By Tester: Supposedly, tester A was correct 31% of the time compared to tester B with 27%, but I think this is misrepresents tester A's ability to correctly identify the color because tester A had only 13 candies total and not 14 like tester B. Both testers had nearly identical distributions in their frequencies of color choices. So, perhaps this is an indication there could be some kind of flavor difference, but not one that either tester is trained to detect. Tester B had better results refreshing their senses using coffee grounds compared to water 75%/25%, whereas tester A was 50/50 between the two. A heatmap by tester suggests that tester B could differentiate between more colors (maybe), as that tester was able to correctly determine 3 of 4 colors, although accuracy was still low. Tester A could only correctly distinguish two colors, but also with low accuracy. A contingency by tester analysis again shows very small likelihood ratios, but indicates that tester A is twice the ratio. I'm not sure about this because of the uneven number of runs between the testers, but maybe the analysis takes this into account?   Any other thoughts would be much appreciated. By the way, I used the JMP 16EA1 to analyze the results (I'm running calculations with JMP Pro 15.0 and don't want to interrupt them).     I think the best thing that came from this is that my son is now excited about doing a DOE -- like a taste test for preference of ice cream toppings or other candies with his friends. So hey, even though we didn't really find all that much from this DOE, it got him interested in doing more and trying something new. For a 10 year old, I think it's great!   Thank you all for your input!, DS        ... View more

Hi @P_Bartell,     Thanks for the feedback and link to BIBDs. I had read a little about those before as well. However, when trying out different designs within that platform, I couldn't quite get the type of design I was thinking of like I could with the Custom DOE. Perhaps it's because I'm just not very familiar with the BIBDs. We might try that at a later date and under a different design.     I ended up going with a Custom DOE that has four levels for the colors, two participants (me and my son), and then two "cleaning" methods in-between samples (taking a drink of water or smelling coffee grounds). I think it'll be fun to do with him, and my wife is going to be the moderator and taking down the responses.     I'll be sure to update the discussion with how things went. I really appreciate everyone's feedback and thoughts on this.   Thanks!, DS ... View more

Hi @statman,     Yes, sorry about the typo. His claim was that by tasting the candy, he could tell the color of the candy shell.     Right now, we have only a single bag of the candy. I do like the idea of adding a second tester to the mix and maybe even cleansing method.     I think this will be fun, especially for him!   Thanks for the feedback.   DS ... View more

Hi @markbailey,     You're absolutely right about evidence not changing his mind! I thought it might be a fun exercise for him to get some experience with DOE and then understanding the outcomes of the analysis. And, he is more than fine with eating more!     I also thought about the change in the response column, and deleting the response limit to analyze in the contingency platform. I might actually do both so as to see the general distribution as well as the contingency.     Regarding the design, do you think I should block it for him to "take a break" so as not to influence the decision making process by tasting too much of the same chocolate? Maybe block it in groups of 5 or so -- four blocks?     Thanks for your thoughts!   DS ... View more

Hi @JWi,     I think this is a great question, and one that you'd think there would be an answer for. I tried it with some test Excel files and scripting, but I couldn't find a way to make JMP get around not "seeing" the hidden tab. I tried multiple work-around attempts and couldn't get it to work. You might have to save a local copy with the tab unhidden to get around this and then run your JSL script on your local xlsx file rather than the shared one. Not an ideal situation, but at least you could continue with your work.     It's likely something on Microsoft's side that won't let JMP see the worksheet.   DS ... View more

Hi @Afnanyasir,     Before dropping the mixture term, A*B, and the quadratic terms, A^2 and B^2, I recommend using the Fit Model platform and testing out the RSM model, but use the GLM, GenReg, or Stepwise "Personalities" to see which terms in the polynomial expansion of the RSM are really insignificant in the model. Try some other methods to fit the data and see what they give. If they're all giving similar results that the A*B and quadratic terms are insignificant, then drop them in the final model. If the data was gathered via a RSM DOE, it doesn't have to be analyzed through that same platform. You can analyze it other ways as well.   Hope this helps, DS ... View more

Hi @Jacksmith12,     This is a really good question, and I did some searching, but couldn't find a scripting way to evaluate the blob (which is what is written into the excel cell), into an image as it's saving the data table as an excel file. Instead, you can save each individual image in the output directory and then insert them with Excel individually. I tried that with the Big Class Families.jmp file and got this:  You might be able to write a Powershell file or some other non-JMP code that loops through the number of rows in the excel file to insert pictures at the appropriate cell.     Here's the code I was playing with to at least get you started -- based on your original section of code: dt = Open( "$Sample_Data/big class families.jmp" ); OutputFolder = "\Desktop\test"; //or wherever you are saving things ColNames = dt << Get Column Names("String"); DesColumnsList = {"pics"}; for(i = 1, i <= N Cols(dt), i++, If(Contains(DesColumnsList,ColNames[i]), Col = Column(dt,i); // Get Reference for Column Col << Set Selected(1); ); ); dt_New = dt << Subset(Selected Rows(0),Selected Columns(1)); Close(dt_New,Save(OutputFolder || "\Test.xlsx")); For (k=1, k<=N Rows(dt), k++, dt:picture[k]<<Save Image(OutputFolder || "\image"||char(k)||".png"); );   It's really the last For loop that does anything new for you.     I don't know how to automate getting the images INTO excel.   Hopefully this is a start!, DS ... View more

Hi @Afnanyasir,     If you have prior knowledge about a model, then just use that functional form.     For example, in physics, if an object falls from height "h" with a certain initial velocity (v0), then the vertical position of the object (as a function of time) is (g is gravitational constant):   y(t) = h + v0*t + (1/2)gt^2     If the experiment you're doing involves falling objects, then you would use this equation and no other, so it's not an empirical formula, but a first-principles formula. If you have a first-principles formula from physics or chemistry, go ahead and use it.     But, based on what you're saying, it sounds like from previous work that you have an indication it's a linear model based on alkali dosage and temp, and just wanted to check if there were any cross terms or higher order terms to it. By running it through a RSM, you can get a good indication as to what factors might be important or not. However, that p-value you point to is more about the parameter estimate value and whether or not it falls within the 95% confidence interval. So, all it means is that the parameter estimate is not very good.     If you want to know whether a term, such as A*B or A^2 or B^2 should be in a model, I recommend running it through the stepwise or GenReg Personalities in the Fit Model platform. You could also try a PLS or GLM personality and see what you find there.     As an example, with the Big Class.jmp example data, you can run an RSM on age using height and weight as "A" and "B". If you do that, the Effect Summary plot tells you which factors (and combinations) are relevant, if their P-value is << alpha. These are the kind of p-values you want to look at.     For your work, it sounds like your response, SCOD Increase is just linearly dependent on Temp and Alkali Dose. Whatever is the simplest model and one that you can verify, then go for that, by all means.     As a last bit of advice, if you have a small data set, you might want to look at validating the model, you should look into the autovalidation JMP add-in and see if that can help you.   Hope this helps!, DS ... View more

Hi @Afnanyasir,     Just to make sure, when you say you applied RSM to the data, do you mean a response surface model? Before applying that specific model, do you have a priori knowledge that this is the correct model to use, maybe from a physical or chemical equation that is known to model the specific kind of behavior? If not, what is the justification for choosing that particular model? If it's not based on a physics/chemistry model I recommend analyzing this with GenReg, which you can do since you have JMP Pro.     The model that you show doesn't look like a complete RSM because, as you show in the equation below, your model doesn't have the mixture term A*B and is missing one of the quadratic terms. Otherwise, your equation is perfectly fine. You have the intercept, the first number, the slope for dependence on Alkali Dose, slope for the dependence on Temp, and slope for the dependence on Temp^2. Did the other terms drop out because they were not contributing to the model?     You can always manually enter in the other terms to get the full RSM and re-run the model. Note of caution: be careful how many terms you include in your model so you don't over-fit. Your current model has three terms in it, so you need at least 4 rows in your data table to ensure that you have at least one degree of freedom. If you are trying to predict an outcome, you'll want to test it out on some data that was not used to fit the model, or at least do some verification runs to make sure your model is correct. If you have enough rows in your data table (maybe >100 or so), you might be able to do cross-validation and find a best-fit empirical model.   Hope this helps, DS ... View more

Hi @aallman,     I'm a little confused as to what exactly you are attempting to do, but it does sound somewhat similar to something I've done before, where I needed to Stack the source data table and then tabulate the output data table, but didn't want to have to re-script everything when I concatenated some new data to the source data table.     Here's some example code of the where I have the Stack script written in the source dt. Data Table( "Analysis" ) << Stack( columns( :X1, :X2, :X3, :X4, :X5, :X6 ), Source Label Column( "Test Apparatus" ), Stacked Data Column( "Data" ) ); Current Data Table() << Set Name( "Data Stack" ) << New Script( "Fit Y by X of Data by Source", Oneway( Y( :Data ), X( :Source ), Means( 1 ), Mean Diamonds( 1 ), By( :Test Apparatus ) ) ); Data Table( "Data Stack" ) << New Column( "Date-Time", "Ordinal", Formula( :Date + :Time ), Format( "m/d/y h:m:s" ) ); Data Table( "Data Stack" ) << Group Scripts( "Run these after stacking", {"Tabulate by Lot (run after stacking)", "Tabulate by Apparatus (run after stacking)"} ); Data Table( "Data Stack" ) << Move Script Group( "Run these after stacking", to last );   And it will carry-over the scripts to the newly stacked data table. I also have saved to the main dt the grouped scripts with the title "Run these after stacking", which also get carried over to the newly stacked data table.     I imagine that you might be able to approach your problem in a similar fashion.   Good luck!, DS ... View more

Very cool coding!   Is it possible to make JMP speak phrases faster? ... View more