How to Handle Missing Data: Complete cases & Imputation

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An introduction to three ways of handling missing data.

00:28 Mammals sleep dataset
02:04 Complete case analysis
03:22 Mean Imputation
04:33 Multiple Imputation
  1. Stats with Mia
  2. Missing data
  3. multiple imputation
  4. statistics
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Комментарии
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You are a relief to listen to. Thank you!

CVR
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Thank you very much! This is really useful!

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

cristina
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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

larissacury
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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...

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

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

swsebvq