Why do we clean data before analysis?单项选择题

A

To remove Python code

B

To make files smaller

C

To improve accuracy and reliability of analysis

D

To make charts colorful

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类似问题

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

Which of the following is part of data cleaning?

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.

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