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Nice work, thanks!

Thank you so much for this video. This saved me!

great video, I think you meant stacked bar chart and not stacked pie chart?

Good illustration, but I couldn't stop wondering (and being sceptical) - in relation to the probit model - of the statement early on that since we CAN'T observe y* (the latent variable), we can simply ASSUME it has a normal distribution. What on earth is the theoretical justification for this? Presumably a similar assumption is implied by the logistic regression's standard functional form (i.e. a linear relationship between the log of the odds ratio (the dependent variable) and the explanatory variables, though the video is completely silent about what the theoretical justificationbof that implied assumption might be...

A really great explanation! Thank you! Some minor mistake in writing: at 14:19 you wrote F_Unlikely = exp(-0.82) but you meant 0.44/(1+0.44) instead. Same for F_somewhat Thanks!

thank you

Honestly, this is exactly what I needed for a refresher for stats work at work. I especially appreciate that you took the time to explain the motivation of IQR. Thank you so much, Dr. Harbaugh

thank you for this helpful video Dr Gregg

Thank you very much.

Wonderful explanation 🥰

Thanks very muuuuuuuch. I wish I had found this video sooner.

This is an amazing tutorial. Thank you very much!

It was very useful. Thank you!

This video sucks. It assumes that the mean doesn't change when a new data point is added. I'm looking for the effect on both mean and standard deviation when the new value doesn't give the same mean and standard deviation as the original sample.

Thank you so much for this useful video! Answered all my questions

dear can you help me i work my study contain likert scale for both dependent and independent variables then how can i analysis using ordinary regression analysis was it possible to transform thier response into other means or sum then use ORA

Amazing video, thanks!

This was an amazing video

Wonderful video, thank you! One question, how would we be able to state the effect of an independent variable on the outcome? For instance, how can I express whether or not public school attendance is significantly associated with an increase likelihood of applying to grad school?

Thank you so much! This is extremely helpful. I do have one question - at 14:20 shouldn't the probability equations be F(unlikely) = 0.44/(1+0.44) = 0.306 and F(somewhat) = 3.58/(1+3.58) = 0.782 instead of exp()?

Really very interesting lecture

THANK YOU SO MUCH!!!! IT WAS REALLY HELPFUL :)

This is the most helpful video talking about the intuitive concept behind the ordered regression that I can found on RUclips. Thank you so much.

9:52

Thanks for this useful video. Can you please tell me how we should deal with these outliers (either mild or extreme ones)? How we should modify it? there is a method called winsorization in which we replace the outlier by the 95th percentile value. For my dataset, when I do that it has some contradictions with the IQR rule.

Thanks! this is very helpful! I'm wondering if there is a way to assess the effect of a predictor X on the thresholds (intercepts) estimation. Are the thresholds assumed to be constant and pre-determined?

Thanks

Thanks for this easy but informativ tutorial! It would be nice if you can do a tutorial on hierarchical ordinal regression! & is there a heuristic on how much categories there sould be at maximum?

The video could have better explained how to arrive at the regression coefficients and intercept when dependent variables have dummy values.

Nice video. thanks for sharing. is there a way to proof that the expected value for a negative binomial is equal to 1-p

This is great. Thank you.

How did you calculate the probability values for the ordinal probit regression model? For the instance with the specific student, how did you calculate the probability of them answering "somewhat" as 0.449?

Thanks for a helpful video. I'm new to ordinal regression and your's was the first video I've watched. The point where I was lost was the animation of bell curve movement with changes in GPA. How can the bell curve move if the value of boundaries is already defined? I can see how we'll get different Y* value with changes in GPA. However, I can't see how bell curve itself will move around on a likert scale!

Wonderful, love it!

Great explanation of the graphs

Amazing introduction and example! Thanks a lot!

Many thanks sir

Thank you very much sir ☺️

Love it, you solved a lot of mysteries in my head. :)

Is the effect size in this example Cramer's V?

Excellent video . Thank you.

In the Probit Regression, how did you get the specific values for the tau cuts?

Thanks for the lesson! Is it possible to provide the dataset you have used here?

How do I know which one (probit or logit) fits my data better?

Amazing walk through!!!!! Thank you so much

Thanks for this

this is an excellent video!! khan academy level quality :) explained a difficult concept in a way that was easy for me to understand and visualize

Thank you.

great tutorial. loved it!

Thanks for the great explanation! When I tried to calculate an odds ratio I only got one value. I have trouble interpreting this value. Do you have any advice on that?