I really appreciate your videos. However it's hard for me to relate the last 4 videos of this series. The first two are pretty clear but I can't really see when to use a Likelihood approach and when to use a confidence interval approach to determine the parameters of a sample. Then we have a whole video on hypothesis testing, in which we use confidence intervals and p-values to acess probabilities of accepting and rejecting hypothesis to finally see another approach of Hypothesis testing, now using Likelihood functions, again. It seems to me that we have a bunch of different tools to assess the same information and it's not clear to me when use each one of them. Your videos are great, thought.
Mr. Justin Z--you state at 13.25 of the video there is additional information in the Textbook. Please provide me information about the Textbook so I can purchase it. Also, the video, although excellent, did not contain any examples. If the examples are in the Textbook, once I get the Textbook I will hopefully have a better understand of the Wald Test, etc. Thank you. WhetstoneGuy
Thanks for the video! I am a bit confused about why a confounder is sometimes a nonsignificant predictor based on likelihood ratio test. By definition a confounder has association with both - exposure and the outcome, and it's not in the causal pathway. So, if a regressor is a confounder, the p value of LR test on it's removal from the model should be significant, right? Why is it the case that regressors sometimes have nonsignificant p value of LR test on their removal from the model but are found to be a confounder as per change-in-estimate criteria? Is this because of type 2 error in LR test where we accepted the null hypothesis when we shouldn't have done so?
Thank you @zedstatistics for this amazing video. Without having much information regarding mathematics I am able to grasp the overall concept and difference between three methods. One thing that might help me would be real-life examples where these tests could be applied. For example anomaly detection, etc.
This work is much appreciated. Thanks a lot for your clear explanation. Could you please provide an example and show how to use the tests?
I was looking for an explanation of the Wald test. This was perfect! Great visual and great explanation. Thank you.
Never been this clear! Appreciate this explanation.
I really appreciate your videos. However it's hard for me to relate the last 4 videos of this series. The first two are pretty clear but I can't really see when to use a Likelihood approach and when to use a confidence interval approach to determine the parameters of a sample. Then we have a whole video on hypothesis testing, in which we use confidence intervals and p-values to acess probabilities of accepting and rejecting hypothesis to finally see another approach of Hypothesis testing, now using Likelihood functions, again. It seems to me that we have a bunch of different tools to assess the same information and it's not clear to me when use each one of them. Your videos are great, thought.
Mr. Justin Z--you state at 13.25 of the video there is additional information in the Textbook. Please provide me information about the Textbook so I can purchase it. Also, the video, although excellent, did not contain any examples. If the examples are in the Textbook, once I get the Textbook I will hopefully have a better understand of the Wald Test, etc. Thank you. WhetstoneGuy
Hello I just need to check if you got sometime and show how is the LRT converges to a Chi-square distribution?
Thanks for the video sir, Could you explain to me why are we multiplying 2 to LR ?
And is score test same as Lagrange multiplier test?
Score test is the same as Lagrange multiplier
Hello there. Which is the textbook that is referred on the end of the video. I would love to follow along!
Thank you so much for this video!! Needed something to explain the big picture
Best explanation I have ever seen! Thanks a lot!!!
Thank you so much! All of your videos are so informative and helpful!
What is the name of the textbook??? Thanks for the work mate
What does it mean that wald test is chi square distributed ? I know what is chi square but can anyone elaborate
Thanks for the video! I am a bit confused about why a confounder is sometimes a nonsignificant predictor based on likelihood ratio test. By definition a confounder has association with both - exposure and the outcome, and it's not in the causal pathway.
So, if a regressor is a confounder, the p value of LR test on it's removal from the model should be significant, right?
Why is it the case that regressors sometimes have nonsignificant p value of LR test on their removal from the model but are found to be a confounder as per change-in-estimate criteria? Is this because of type 2 error in LR test where we accepted the null hypothesis when we shouldn't have done so?
Thank you very much. I’m grateful
Hi, thank you for this video. Shouldn't there be absolute value for theta_hat - theta_0 on 7:02 ?
What is the title of the text book?
Thank you @zedstatistics for this amazing video. Without having much information regarding mathematics I am able to grasp the overall concept and difference between three methods. One thing that might help me would be real-life examples where these tests could be applied. For example anomaly detection, etc.
Great video, Very clear, thank you very much!
Amazing work here. thank you so much
is score test the same as white test?
I love your videos very much .
What is the textbook you are referring to?
You are great! Thanks for the explanation.
Thanks!
Saved my life
What are tthe prerequisite videos for this?
Perfect explanation
have a look on zero.
zero is good.
u need to be louder & pronounce words clearly in ur videos
Great video. It is so much clearer than my prof.
Thank you for the insight!! Much appreciated. Nice colour scheme it's easy on my eyes
Awesome!
Thanks!!! That's a great video and intuitive explanation!
All these concepts have finally clicked.. Thank you!
Thank you very much, your work is great!
Much appreciated for your explanation!
Thanks!
Very clear, thanks!