Which of the following is part of data cleaning?单项选择题

A

Designing charts for presentations

B

Comparing two columns

C

Fixing inconsistent values like Miamii and Mimi

D

Writing a summary report

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Question textMatch the descriptions and examples to the following dirty data types: [table] Dirty Data Type | Description | Example Codings | Answer 1 Question 17 Different values used to represent the same valueValues within the same attribute have different formattingValues are spelt differently and/or incorrectlyValues within the same attribute have different unitsAttributes have the incorrect data type, preventing certain operationsValues far outside the normObservations appear multiple timesValues missing for a variable, some values missing for an observation Values that themselves violate range constraintsValues that violate constraints across multiple attributes | Answer 2 Question 17 NA vs N/A vs Not ApplicableUppercase vs lowercaseMELBOURNE VS MELBOURN VS NELBOURE100cm vs 1m15 vs "15"Typos or unrealistic valuesExact or almost exact duplicate observationsNegative pricesCalculation attributesEmpty string Data type | Answer 3 Question 17 Different values used to represent the same valueValues within the same attribute have different formattingValues are spelt differently and/or incorrectlyValues within the same attribute have different unitsAttributes have the incorrect data type, preventing certain operationsValues far outside the normObservations appear multiple timesValues missing for a variable, some values missing for an observation Values that themselves violate range constraintsValues that violate constraints across multiple attributes | Answer 4 Question 17 NA vs N/A vs Not ApplicableUppercase vs lowercaseMELBOURNE VS MELBOURN VS NELBOURE100cm vs 1m15 vs "15"Typos or unrealistic valuesExact or almost exact duplicate observationsNegative pricesCalculation attributesEmpty string Missing values | Answer 5 Question 17 Different values used to represent the same valueValues within the same attribute have different formattingValues are spelt differently and/or incorrectlyValues within the same attribute have different unitsAttributes have the incorrect data type, preventing certain operationsValues far outside the normObservations appear multiple timesValues missing for a variable, some values missing for an observation Values that themselves violate range constraintsValues that violate constraints across multiple attributes | Answer 6 Question 17 NA vs N/A vs Not ApplicableUppercase vs lowercaseMELBOURNE VS MELBOURN VS NELBOURE100cm vs 1m15 vs "15"Typos or unrealistic valuesExact or almost exact duplicate observationsNegative pricesCalculation attributesEmpty string Outliers | Answer 7 Question 17 Different values used to represent the same valueValues within the same attribute have different formattingValues are spelt differently and/or incorrectlyValues within the same attribute have different unitsAttributes have the incorrect data type, preventing certain operationsValues far outside the normObservations appear multiple timesValues missing for a variable, some values missing for an observation Values that themselves violate range constraintsValues that violate constraints across multiple attributes | Answer 8 Question 17 NA vs N/A vs Not ApplicableUppercase vs lowercaseMELBOURNE VS MELBOURN VS NELBOURE100cm vs 1m15 vs "15"Typos or unrealistic valuesExact or almost exact duplicate observationsNegative pricesCalculation attributesEmpty string Variable range validation | Answer 9 Question 17 Different values used to represent the same valueValues within the same attribute have different formattingValues are spelt differently and/or incorrectlyValues within the same attribute have different unitsAttributes have the incorrect data type, preventing certain operationsValues far outside the normObservations appear multiple timesValues missing for a variable, some values missing for an observationValues that themselves violate range constraintsValues that violate constraints across multiple attributes | Answer 10 Question 17 NA vs N/A vs Not ApplicableUppercase vs lowercaseMELBOURNE VS MELBOURN VS NELBOURE100cm vs 1m15 vs "15"Typos or informative extreme situationsExact or almost exact duplicate observationsNegative pricesCalculation attributesEmpty string [/table]Check Question 17

What should you do when you find a value that is impossible, such as a negative age?

Question at position 2 Why is it important to manicure data?To give the impression that the data is changed in a devious manner.To manipulate the data in an unscrupulous manner.It is not important to manicure dataRaw data seldom meets the requirements for processing and analysis.

Which of the following is part of data cleaning?

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