Handling Missing Data Quiz

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Handling Missing Data Quiz, Handling Missing Data

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Questions:

1. Which of the following is a common method for handling missing data?

• 1. Deleting rows with missing values
• 2. Using the mean to fill missing values
• 3. Using a machine learning algorithm to predict missing values
• 4. All of the above

2. What is the term used for removing rows or columns that contain missing data?

• 1. Imputation
• 2. Deletion
• 3. Interpolation
• 4. Normalization

3. Which imputation method replaces missing values with the mean, median, or mode?

• 1. Random sampling imputation
• 2. Regression imputation
• 3. Central tendency imputation
• 4. K-nearest neighbors imputation

4. What is the potential drawback of deleting rows with missing data?

• 1. It is computationally expensive.
• 2. It can lead to biased results.
• 3. It always improves model accuracy.
• 4. It requires complex algorithms.

5. Which technique involves predicting missing values based on other available data?

• 1. Listwise deletion
• 2. Pairwise deletion
• 3. Multiple imputation
• 4. Hot deck imputation

6. Which of the following is NOT a method for handling missing data in time series analysis?

• 1. Forward fill
• 2. Backward fill
• 3. Interpolation
• 4. Cross-validation

7. In the context of handling missing data, what does ‘MCAR’ stand for?

• 1. Missing Completely at Random
• 2. Missing Conditional on Available Rows
• 3. Missing Characteristic Attribute Reduction
• 4. Missing Completely Available Records

8. Which method is most suitable for handling missing data when the data is ‘MAR’ (Missing At Random)?

• 1. Listwise deletion
• 2. Multiple imputation
• 3. Mean imputation
• 4. Mode imputation

9. Which Python library is widely used for data manipulation and handling missing data?

• 1. NumPy
• 2. Pandas
• 3. SciPy
• 4. Matplotlib

10. Which of the following is a disadvantage of using mean imputation?

- A) It is computationally intensive.
- B) It can distort the variance of the data.
- C) It requires labeled data.
- D) It is only applicable to categorical data.

Answers:

1. 4. All of the above
2. 2. Deletion
3. 3. Central tendency imputation
4. 2. It can lead to biased results.
5. 3. Multiple imputation
6. 4. Cross-validation
7. 1. Missing Completely at Random
8. 2. Multiple imputation
9. 2. Pandas
10. 2. It can distort the variance of the data.

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