Fantastic tutorial Julian, I'd love to see you expand on some of the methods you highlighted and introduced @ the end of this video (Heirarchical Clustering, Latent Class, Principal Components) in a separate video.
I become very happy from biometrics analysis in jmp,so researchers should be keep it up to find world problem solver technology system for un solved problem.
Hi there. Thanks for this wonderful explanation of how to use JMP. I am very thankful for your explanations and I look forward to learning more from you about this tool!
Hi Saad -- that shaded region is the confidence region. Like a confidence interval for a point estimate (like a mean), the confidence region or confidence band is a representation of uncertainty about where that linear relationship is in the population from which the current sample was drawn. In other words, the different lines that could be in that region all reflect reasonable candidates for the true relationship between the Y and X variables. Here's the link to the wikipedia page for more information: en.wikipedia.org/wiki/Confidence_and_prediction_bands
What if a data has a lower end and an upper end value which should be treated as one unit vs another unit? For example: Min temperature and max temperature of a water vs fish population or such
I think you can use one-hot encoded categorical variables but in general it's best to really pay attention to both your data type and modeling type in JMP! There is a reason why these two pieces of metadata are so important for each column [and are the first two column properties you see for every column in JMP] because they tell JMP which models are appropriate for your particular data situation (graphs/analyses that are not appropriate will not be selectable in JMP's workflow). For those that are unfamiliar with one-hot encoding here is a good intro read: www.educative.io/blog/one-hot-encoding#what
Best JMP video that I found
What a great demonstration / explanation and visualizations of how to perform multivariate analysis. Thanks!
Great tutorial, fantastic explanation!
Great tutorial, thanks, Dr.!
You are amazing, this video has given me such an appirecation for JMP
Very very effective explanations - I sincerely thank you for uploading these videos.
Fantastic tutorial Julian, I'd love to see you expand on some of the methods you highlighted and introduced @ the end of this video (Heirarchical Clustering, Latent Class, Principal Components) in a separate video.
amazing tutorial! I'm working with Jump and didn't know most of the tricks
This is the best JMP video I've watched! You're such a great instructor!
Wonderful tutorial. Very helpful thank you and I appreciate it.
I wish all the teachers in tutorials were like you! I am amazed by your videos on JMP Pro!
Very helpful your tutorial, thanks
Super helpful. Thanks.
I become very happy from biometrics analysis in jmp,so researchers should be keep it up to find world problem solver technology system for un solved problem.
Excellent. Thank you, sir.
Great analysis thanks alot
wonderful sir, couldn't understand but love your voice
Thank you!
Brilliant, cheers!
This video was very well done! Thanks for sharing! Thinking about making something similar in other languages! 😁
Great tutorial!
Thank you, Tyler! I'm so glad it was helpful
Hi there. Thanks for this wonderful explanation of how to use JMP. I am very thankful for your explanations and I look forward to learning more from you about this tool!
Always positive reviews with you....
nice...
you sound like a young, optimistic Sam Harris! ;)
Thanks for the tutorial. Just a quick one, what does the pink area around the line-of-best-fit represent?
Hi Saad -- that shaded region is the confidence region. Like a confidence interval for a point estimate (like a mean), the confidence region or confidence band is a representation of uncertainty about where that linear relationship is in the population from which the current sample was drawn. In other words, the different lines that could be in that region all reflect reasonable candidates for the true relationship between the Y and X variables. Here's the link to the wikipedia page for more information: en.wikipedia.org/wiki/Confidence_and_prediction_bands
Julian Parris Thanks very much Julian
What if a data has a lower end and an upper end value which should be treated as one unit vs another unit? For example: Min temperature and max temperature of a water vs fish population or such
@Julian, would it be acceptable to use onehot encoded categorical variables?
I think you can use one-hot encoded categorical variables but in general it's best to really pay attention to both your data type and modeling type in JMP! There is a reason why these two pieces of metadata are so important for each column [and are the first two column properties you see for every column in JMP] because they tell JMP which models are appropriate for your particular data situation (graphs/analyses that are not appropriate will not be selectable in JMP's workflow). For those that are unfamiliar with one-hot encoding here is a good intro read: www.educative.io/blog/one-hot-encoding#what
Is the dataset available for download? Would really be beneficial to follow along.
How to do significane level test