This is definitely one of the best tutorial videos I ever watched. I would request to publish more such videos. One thought Hans, the "Year" variable you were putting in the Regression, I might avoid it. "Year" is not a continuous variable and might give a wrong interpretation.
Great video - clear explanation! Why would adding the year cause this to be statistically significant? Are there approaches I can take to ensure all appropriate variables are in the model formula?
I think the only missing step would be to apply clustered errors to account for serial correlation between observations from before and after treatment.
This is definitely one of the best tutorial videos I ever watched. I would request to publish more such videos. One thought Hans, the "Year" variable you were putting in the Regression, I might avoid it. "Year" is not a continuous variable and might give a wrong interpretation.
Great video - clear explanation! Why would adding the year cause this to be statistically significant? Are there approaches I can take to ensure all appropriate variables are in the model formula?
I think the only missing step would be to apply clustered errors to account for serial correlation between observations from before and after treatment.
Is it worth interpreting the other Betas as well? IE. Beta0, Beta1 and Beta2.
Hi, Nice learning, Could you share python code and data set?
If I have a low R2 value but my interaction term is significant, can I still consider it to be valid?
This is very helpful. Thanks a bunch buddy.
Thanks for the clip. I wonder if you can provide full code along with the link to the file! :)
Love you man for this
Hi, Thank you for the amazing tutorial. Could we please access the data to work on it?
Can we get the data please so we can practice?
Very helpful! Thanks so much!
thanks teacher please can you share with me this file
Maravilha!