Updated with new video April 2024.
Why use DOE and the Guided mode of Easy DOE?
- More rapidly answer “what if?” questions
- Identify important factors when faced with many
- Do sensitivity and trade-space analysis
- Optimize across multiple responses
- By running efficient subsets of all possible combinations, one can – for the same resources and constraints – solve bigger problems
- By running sequences of designs one can be as cost effective as possible and run no more trials than needed to get a useful answer
See how to:
- Start with the end by presenting DOE results interactively to decision makers
- Use the 6-step DOE Process implemented in the Easy DOE interface
- Understand factor types supported and model choices
- Use Guided Easy DOE process for slightly more complex 3-response, 4-factor, trade-space/optimization example using new .jmpdoe file.
- Define
- Specify
- Design
- Enter Data
- Analyze
- Predict
- Report
Questions answered by Tom Donnelly @tom_donnelly and Wendy Tseng @wendytseng at the live 2024 webinar:
Q: What if the factors are hard to change?
A: If you have hard-to-change factors, you need to use the Flexible mode in Easy DOE (vs. the Guided mode which is today's focus).
Q: How are the factors determined?
A: The experimenter is the one who determines which factors they want to study.
Q: How do you repeat the same results if they change each time? Is there a seed number we can control?
A: The simulator is simply a way to "run the DOE" since we don't have a lab. This allows us to design and analyze an experiment in this demonstration. (One could remove the random error in the simulator.)
Q: Why would you add a response without a goal?
A: There is an option to Add a Response without a goal (not a goal without a response) because you either do not have a goal for the factor yet or you simply do not have one (you just want to observe the response).
Q: What is the formula for the simulator columns?
A: s a formula that Tom built to make the experiment's results interesting. He made it up. Think of it as a way to collect data in a lab instantaneously for the purposes of this demo.
Q: Is there a table option to add p-values in the model estimate window?
A: You can right-click in the table and add the p-value. Right click > Columns > Prob > |t|
Q: What does it mean when JMP zeros the least significant factors?
A: The term is removed from the model. It does not play a role in changing the responses (i.e. speed, contrast)
Q: What does the scale refer to, in between term and the estimate column?
A: It represents the estimate for the model term. it is graphing the estimate values (estimate and the lower and upper 95%).
Q: What does the estimate indicate?
A: Model estimates are how the terms relate to the response.
Q: How can we reduce noise in DOE?
A: You can add more runs to your DOE.
Q: How does JMP create the sample data for modeling the relationship?
A: If you are asking about Simulate responses, JMP uses a normal distribution with mean of 0 a std dev of 1 as a default to add error to the model specified in the design with the coefficients you type in.
Q: If there is no replicates, how do you estimate error?
A: JMP's recommended number of runs includes more than the minimum number of runs to estimate the model (more runs than the # of model terms). Guided mode ensures that you have enough runs to get model error. (Replicates are necessary for pure error and testing lack of fit.)
Q: So, am I correct that one use for the Easy DOE is as a screening study?
A: Yes, you can use Easy DOE to design screening studies or optimization studies (or something in between).
Q: So essentially you need to run the experiments in to get data to simulate your model?
A: Yes, in real life, you are in Easy DOE to design the experiment. Then you go into the real world (e.g., lab) to collect your data. Then you type the data into Easy DOE (or load the data table) and do the analysis.
Q: Do you have an example with categorical x and y values?
A: You are seeing an example with categorical factors (X's). Categorical Y's are possible but a much more advanced topic (to ensure you are collecting enough data for a useful model).
Q: How does DOE result change if you switch one of the responses from minimize to maximize or vice versa?
A: the design is driven by the model choice. However, I can set the desirability, and for example change maximizing MOP to minimize MOP. This is going to change the answer, and then I reanalyze now that I've made that change when I optimize. In our case it the answers were pretty similar, but normally I would think if you were changing the goal there would be a difference. See video:
Q: So by default, no replicates are added? Do we need to add them separately?
A: You can add replicates using the Flexible (more advanced) Mode.
Flexible Mode lets you add replicatesFlexible Mode lets you add replicates
Q: In many of the experiments that are ran by my team, many of the continuous factors are ran as discrete numeric factors. Are there any advantages and disadvantages for using this approach?
A: The only advantage would be, some people want more intermediate levels. If you're ultimately going to fit a quaradratic model, all you need are 3 levels. For example, to force 5 levels and you just pick a discrete numeric and give it 5 levels, JMP will probably put them in. Then JMP will pick way more of the extremes and fewer in the middle, because mathematically it's still thinking continuously with it. There is also more leverage at the extremes with categoricals. In summary that is a way to force more levels for a continuous, but if it's truly continuous, probably you don't need to do that.
Q: I use another DOE software that usually contain a set of lack-of-fit runs in a design. How was lack of fit tested in JMP DOE design?
A: Replicates will always give you a lack of fit test, because then we have an estimate of pure error that can be compared to the model error. If you don't have any replicates, we don't give you a lack of fit test but give you the same model that it will have some model error. If you've been making the same product for years, you probably already have a good historical idea of what the pure error is. One reason to do replicates nad tget the lack of fit test is if you are new to the process.
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