The correct answer is: A. Tidy datasets are all alike but every messy dataset is messy in its own way.
A tidy dataset is a dataset in which each variable forms a column, each observation forms a row, and each value of a variable is unique. This makes it easy to identify the relationships between variables and to perform statistical analysis.
A messy dataset is a dataset that does not meet the criteria of a tidy dataset. This can be due to a variety of factors, such as missing values, duplicate values, or inconsistent data types. Messy datasets can be difficult to analyze and interpret.
The statement “Tidy datasets are all alike but every messy dataset is messy in its own way” is incorrect because there are many different ways for a dataset to be messy. Some common types of messiness include:
- Missing values: A missing value is a value that is not present in a dataset. Missing values can occur for a variety of reasons, such as data entry errors or incomplete records.
- Duplicate values: A duplicate value is a value that appears more than once in a dataset. Duplicate values can occur for a variety of reasons, such as data entry errors or when the same data is recorded in multiple places.
- Inconsistent data types: Inconsistent data types occur when the same variable is recorded in different data types in a dataset. For example, a variable might be recorded as a number in one row and as a string in another row.
These are just a few of the many ways that a dataset can be messy. It is important to be aware of the different types of messiness so that you can identify and address them when working with data.
B. Most statistical datasets are data frames made up of rows and columns is a correct statement. A data frame is a two-dimensional data structure that is commonly used in statistics and data science. It is made up of rows and columns, where each row represents an observation and each column represents a variable.
C. Tidy datasets provide a standardized way to link the structure of a dataset with its semantics is a correct statement. The structure of a dataset refers to the way in which the data is organized, such as the number of rows and columns. The semantics of a dataset refers to the meaning of the data, such as the units of measurement and the definitions of the variables. Tidy datasets provide a standardized way to link the structure of a dataset with its semantics, which makes it easier to understand and analyze the data.