How to Handle Missing Data: Complete cases & Imputation

[@desireedbs]

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

[@11nul1nul]

You are a relief to listen to. Thank you!

[@chonnipvpsk627]

YOU ARE A LIFE SAVER!

[@larissacury7714]

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

[@diegoguisasola3858]

Thank you very much! This is really useful!

[@cristina4366]

Wonderful! Looking forward to the next chapter, comparing the methods, should be very interesting :)

[@cristina4366]

Mia, your explanations are wonderful and delightful. I return to these videos frequently, whenever I encounter complex missingness, so thank you. Really hoping we get to see the video you mentioned, about comparing the estimates you get with each method in R!!

[@cristina4366]

Ohh and last, I am not sure, but I think gestation in this data is rather too much for weeks, ¿perhaps measurement is in days?

[@hamnaya]

This was very helpful! Thank you!

[@mukeshkumar-kh2fh]

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...

[@محمدالنوراني-خ2م]

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

[@alishamahajan9463]

how to check whether the data is mar, mcar or mnar?

[@ananyasingh399]

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