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Great video. Thanks.

Thank you!

Thank you for explaining this so clear. I am in the opposite side of the world a few years after your publication and learning a lot from your videos. =)

Thank you for this clear explanation

Thank you for explaining it so clear!

These video lectures are super helpful for explaining these concepts in a different way that my professor doesn't! Please keep these videos public! Thank you

These presentations are so valuable! Thanks from Belgium!

Good explanation

great video! very clear and helpful!

Thanks for the info.

Excellent overview. Thank you. A more likely (less politically charged) scenario for the relationship not being geographically consistent would be that, in places where there are more people living in the same household wearing a mask is less effective in preventing disease because you have more chances of getting infected. In fact, this was observed during the COVID-19 pandemic in urban areas. Reference: www.ncbi.nlm.nih.gov/pmc/articles/PMC8328572/

Great video. Thanks! Lagrange Multiplier test helps decide between spatial lag and spatial error model, if the project background is unclear on whether there are missing factors or whether there’s geographical dependence.

Thanks foe the video, it was easy to understand and follow.

Super❤

Thank you, that was really helpful!

I'd be really cool if you could do a series on spatial statistics with a playlist. There aren't many videos online. It's a large task to take on though. Thanks

I wish I've seen your videos earlier. Thanks for these videos sir!

This is hugely helpful, thank you.

Low sound

thank you for your explanation

thank you very much

Fantastic! Very helpful.

I hope your students appreciate your clear and concise explanations and expectations. And that you are using data related to a contemporary, real-life problem. While they might not suspect it, I bet this lecture was motivated by your reflections on how students in past years have botched this assignment. Thanks for making this available! Also, that is a very large sheet of butcher's paper.

Thanks for nice explanation :) But I have a question I really need an answer to, so please if anyone could help... 1. I fitted multiple spatial regression models - spatial lag model, spatial error model and spatial durbin model and more. My question is, how do I check the assumptions of normality and homoscedasticity of errors? In classical linear regression, the diagnostics is done on standardized or studentized residuals, but how do I standardize residuals in these spatial models? When I use raw residuals from the spatial models and do QQplot, they always have this "S" shape. Does it mean the models are wrong? 2. The same for weighted version of spatial error model. Which residuals should I use to check the model assumptions?

Hi, I need some help with probability. How can I send/ask my questions?

Thank you!