Probit and Logit Analysis

Год: 2011
Автор: Tej Narayan Bhusal
Издательство: LAP Lambert Academic Publishing
Описание:
Discrete data are frequently encountered in the entomological and pathological science of agriculture. The relationship between dose and response has been tested by using the probit and logit anaysis in this book. From the analysis, LC50, LT50, LD50, goodness of fit, anlysis of deviance, etc were tested. Rsults revealed that discrete dose-response data on entomological study is best fitted by using either log-probit or log-logit regression rather than probit and logit regression only. The detainled on work is present inside the book. I hope it help more for the professionals, researcher, students who involved basically in entomology and pathology of agriculture.

25 Replies to “Probit and Logit Analysis”

  1. Thank you so much! You explain the concepts in a very easy to understand fashion.

    Could you pls let me know whether this video belongs to a certain playlist, so ppl can watch the entire series. Thanks again.

  2. My absolute favorite part of statistics, determining MLE

  3. Ive been learning statistics for the past 2-3 years and your videos have always been helpful!! Thank you so much

  4. I spent the last hour watching this video and the 3 MLE intro ones. Great work! Question: If I want to fit a logit model but add a constraint that my y-values range from 0 to a constant less than 1 (.4 for example), can I simply adjust my function from F[a+bx] to .4F[a+bx] and follow the same steps to maximize a and b? Thanks

  5. You have no idea how helpful this is for people trying to learn data science, this is also the cost function for logistic regression.

  6. Hey Ben. Your videos are really helpful. Do you have any videos for Tobit and truncated models ?

  7. But the product of the probabilities of N obervations becomes smaller and smaller as N becomes larger, since the probabilities are between 0 and 1. True? Does that even matter?

    1. Yes, but you always look for parameter estimates that are in between 0 and 1 since you will interpret them as percentage changes in the dependent variable y.

  8. i dont understand where logit and probit even came into anything tho — u didnt use the models within the estimation i swear???

  9. thank you. why professor we are using as dependant variable the probability although we have a qualitative variable( what s thelogic behind).

  10. what is the difference between cost function and maximum likely hood estimation in logistic regression. please explain.

  11. Thanks so much for your great explanations!! Is there any chance you could make a video about partial effects in the Probit and Logit models?

  12. Thank You so much Ben, I have tried a lot to understand this concept from other content present online but this is the best which I got so far.
    Thanks again.

  13. This helped me so much to understand likelihood. Thank you so much brother.

Comments are closed.