Absolutelu perfect! Thank you for explaining everything with so much ease. Subscribed!

Thank you, Mia. You explained it in the simplest way possible.

Hello .. I distributed my data to the same people for 3 different time periods. In each period, a certain number of questions are answered as part of the questionnaire. That is, in each period, a part is distributed. Accordingly, the answers to the questions of periods 2 and 3 represent missing data in period 1. What does this type of missing data represent and how do I handle it?

Your voice is so soothing... Therapeutic...

Excellent!

Cute, but not helpful. The notation is poorly explained and leads to great confusion. Without explaining and demonstrating Rubin's difficult notation through clear and simple cases, everything else just goes to waste. For instance 'Missingness depends only on observed data' seems improperly defined or even like an instance of circular reasoning: Missingness means that the data is not observed, so which data you 'depend only on' itself depends on the missingness. That's not a clear definition. If you really want to help us, please focus on clear and thorough explanation, not on acting cute.

Such a sweet and fun way to explain missingness! I was struggling to understand these and here you are, Savior!! Many thanks to You!! 😁♥

Great explanation, thanks!

Thank you so much you explained it great

I thought missing at random did not rely on itself but rather other variables?

You are a relief to listen to. Thank you!

Thanks for the videos! I was waiting for R and stata codes for imputations

it's amazing 🤘

Thank you for this video. I am trying to perform the Little's test using python. Most solutions online weren't really useful, so was wondering if there is a step by step methodology somewhere (video, book etc.)

your explanation and illustration are a perfect duo for clearing up this concept

hii mia lot's of love from Bharat❤‍🩹❤‍🩹❤‍🩹

Perfect explanation!

Hi.. What if my dataset has categorical variables too? will this still work?

Amazing explanation!! Thank you!!

hello Ms. Mia how can I contact you? I need help.

thank you for the video. very easy to understand!

Using this test gives me a completely right answer: the missing data is MCAR? I really love your learning style.

I was looking for this hands-on, helpful in finding missing value patterns!

Amazing work, our entire quants team loved your explanations. Keep posting!

Great explanation; please make more such videos!

this helped me prepare for my data science quiz, thank you very much

This is brilliant teaching. Thank You.

Thanks! I enjoyed this video; however, in R, the "naniar" package's function is only reliable (and only runs) when the dataset is equal or less than 30 variables. Other functions are similarly limited. Are you aware of any alternatives?

thanks

Yes! Just what I'm studying right now 😭😭, thank you

I'm so glad I found this channel

Thank you for this amazing video!!

Great examples, thank you !!!

YOU ARE A LIFE SAVER!

Big thanks!

great video! I was struggling with this concept but this video helped greatly!

Thank you VERY much!!! Is it correct to say that the complese obs approach is more realiable then the mean imputation approach given the beta values estimates on the last slide ? I mean, it seems to me that the complete cases betas are more likely to be closer to the multiple imputation ones

Wow!!! thank you SO SO MUCH! SO clear! In case NAs are missing due to the fact that participants didn't show up (for no specific reason, they simply didn't show up) on the test day, it would be a MCar case, right?

Loved your voice TBH

Thank you Mia for this tutorial. I found it Insigthful.

Great video! I was wondering for the MAR case, how do we know for sure that the two groups can be separated into outside/inside pieces? If we didn't know beforehand Mr. Pickles removed more outside pieces than inside pieces, in theory there could have been another unknown property responsible for the differing probabilities of missingness correct? Would this still be considered MAR? E.g. Mr. Pickles only removed pieces that had dirt on them (and they just happened to be mostly outside pieces). Thanks!

Love cats and love statistics! So obviously I subscribed 🥰

Was looking for a good explanation of missing data. Fell in love with your voice 💘.

Mia you're a genius at explaining. Why do you not have more followers :(

sir can we replace NaN value of column by mean in such a way that if other parameter value is in a particular range than find the mean and replace . Example..if column BMI has NaN value then if age of that person is 45 then we first find the mean BMI of people with a age of range 40 to 50 and replace with this.Similarly,for other person have NaN BMI ... then first check the age of that person and set an interval age and find mean and replace...

Nice Explaination !!! Keep the good work ...

First of all, thank you for existing. i really amazed by how calm and beautiful your voice is. second, please keep going T_T i really love your explanation. Instant subscribe!

Hello ,Mia ..its a great video , thanks a lot