Practice Types Of Missingness (2.2.1) - Data Wrangling and Feature Engineering

Practice: Types of Missingness

Lesson 2.2.1Data Wrangling and Feature Engineering

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9 questions

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Exercise
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Quiz
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6 sample questions

Question 1.
What does MCAR stand for?
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Remember the complete randomness of missing entries.
Question 2.
Can you give an example of MAR?
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Think about patterns in missing responses.
3Question 3.
What type of missingness is MCAR?
- Random with bias
- Completely random
- Dependent on other data
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Think about whether randomness affects outcomes.
4Question 4.
Is MAR always a major source of bias?
- True
- False
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Recall how observed data can help understand missingness.
Question 5.
You have a dataset containing survey responses regarding financial habits. The highest earners tend to skip the income question. Identify the type of missingness and suggest a handling strategy.
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Refer back to definitions to identify the missingness.
Question 6.
In a dataset describing health outcomes, some individuals do not answer questions about their health while others do, based on their age. Identify the type of missingness and explain the remedy.
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Think about how observed data relates to the missing values.

5 more questions in this section. Enroll free to practise them all.