The correct answer is D. All of the mentioned.
Messy data is data that is difficult to understand, use, and analyze. It can be caused by a variety of problems, including:
- Column headers are values: This means that the column headers are not actually labels for the data in the columns, but are instead values themselves. This can make it difficult to understand what the data is and how it should be used.
- Variables are stored in both rows and columns: This means that the same variable is stored in both the rows and columns of the data. This can make it difficult to identify the correct data for each variable and to analyze the data.
- A single observational unit is stored in multiple tables: This means that the same data is stored in multiple tables. This can make it difficult to keep track of the data and to analyze it.
All of these problems can make it difficult to understand, use, and analyze messy data. This can lead to errors in analysis, incorrect conclusions, and wasted time and resources.
To avoid these problems, it is important to clean and organize data before it is analyzed. This can be done by identifying and correcting the problems listed above, as well as by removing any unnecessary data. Cleaning and organizing data can be a time-consuming process, but it is essential for ensuring the accuracy and reliability of analysis.