in probit analysis for LC50 determination, in "sample sizes" my experiment was realized in triplicate, i have to use the mean of the experimental group (each replicate was n=10) or the entire group value (10 per replicate n=30)?
I am not sure if I understand the question. If you have an experimental factor X that takes three values and you want to calculate what value of that factor would be needed to get 50% kills, you would pool all data (n=30) into a probit. Then set the predicted value to .5, and solve for the required X using the estimated probit coefficient.
I think I have the same question - for example, I'm testing what is the lethal temperature 50 (LT50) for a fish, so I have 4 treatments: 20C, 21C, 22C, 23C and for each treatment I have 3 replicates with 5 fishes each (n=15; N=60). Then I would like to run a Probit analysis; I should then pool the mortality data of these three replicates, so I have a single value , instead of three values of mortality. Is this correct? Thank you very much.
@@robertapereira7896 If the probit model is correct model for this effect, which it might well be, you would pool the data, run probit, set predicted value to .5 and solve for temperature. See www.researchgate.net/post/How-to-calculate-LD50-value-by-using-Probit-analysis I would probably go with probit model for this problem, but I am not an expert on killing fish with temperature.
Thanks for the video, I was wondering how can I know when I'm in a situation where the probit or logit model is best. My teacher told us that a good way to know is if we know that our error terms (ui) behave like a normal function or a logistical function, but we shouldn't be able to test the error terms (ui) I guess. Sorry if my question is dumb, this is the first time I'm learning about econometrics
The models are so similar that in practice it does not make a difference which one you apply. If I put the logistic distribution and normal distribution side by side, you would not know the difference by plain eyet. en.wikipedia.org/wiki/Logistic_distribution Also, the error terms here refer to the latent variable formulation of these models and you cannot really test the distribution of a latent error term because that error term is not observed and cannot be estimated by a residual.
I do not use SPSS myself and generally focus more on explaining concepts than specific software use. There are a lot of screencasts on UA-cam made by others that explain how to do the analysis on SPSS:
![Logistic Regression [Simply explained]](http://i.ytimg.com/vi/C5268D9t9Ak/mqdefault.jpg)
short and sweet straight to the point! what a great guy
You are welcome!
3 minutes without blahblah 👍👍👍
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thank you so much, sir, clear explanation
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Thank you from Perú
You are welcome.
in probit analysis for LC50 determination, in "sample sizes" my experiment was realized in triplicate, i have to use the mean of the experimental group (each replicate was n=10) or the entire group value (10 per replicate n=30)?
I am not sure if I understand the question. If you have an experimental factor X that takes three values and you want to calculate what value of that factor would be needed to get 50% kills, you would pool all data (n=30) into a probit. Then set the predicted value to .5, and solve for the required X using the estimated probit coefficient.
I think I have the same question - for example, I'm testing what is the lethal temperature 50 (LT50) for a fish, so I have 4 treatments: 20C, 21C, 22C, 23C and for each treatment I have 3 replicates with 5 fishes each (n=15; N=60). Then I would like to run a Probit analysis; I should then pool the mortality data of these three replicates, so I have a single value , instead of three values of mortality. Is this correct? Thank you very much.
@@robertapereira7896 If the probit model is correct model for this effect, which it might well be, you would pool the data, run probit, set predicted value to .5 and solve for temperature. See www.researchgate.net/post/How-to-calculate-LD50-value-by-using-Probit-analysis
I would probably go with probit model for this problem, but I am not an expert on killing fish with temperature.
Thank you
You're welcome
Thanks for the video, I was wondering how can I know when I'm in a situation where the probit or logit model is best. My teacher told us that a good way to know is if we know that our error terms (ui) behave like a normal function or a logistical function, but we shouldn't be able to test the error terms (ui) I guess. Sorry if my question is dumb, this is the first time I'm learning about econometrics
The models are so similar that in practice it does not make a difference which one you apply. If I put the logistic distribution and normal distribution side by side, you would not know the difference by plain eyet. en.wikipedia.org/wiki/Logistic_distribution Also, the error terms here refer to the latent variable formulation of these models and you cannot really test the distribution of a latent error term because that error term is not observed and cannot be estimated by a residual.
@@mronkko thanks a lot for answering the questions, this was really helpful, cheers!
Please upload a video on probit model analysis in spss
I do not use SPSS myself and generally focus more on explaining concepts than specific software use. There are a lot of screencasts on UA-cam made by others that explain how to do the analysis on SPSS: