True or False: Data reduction can only be applied to rows (observations) but not to columns (variables) in a given dataset.

True or False: Data reduction can only be applied to rows (observations) but not to columns (variables) in a given dataset.

• A. True
• B. False
• C. Only for categorical data
• D. Only for numerical data

Answer: (B)

Data reduction is a key statistical technique that refers to the process of reducing the volume of data while maintaining its integrity and usefulness for analysis. This technique can be applied to both rows (observations) and columns (variables) in a dataset. In the context of rows, data reduction often involves removing redundant or irrelevant observations or summarizing data points, such as through aggregation. When it comes to columns, data reduction may include techniques like feature selection, which eliminates unimportant variables, or dimensionality reduction methods such as Principal Component Analysis (PCA), which transforms a large number of variables into a smaller set of uncorrelated variables. The statement that data reduction can only be applied to rows and not to columns is incorrect, as reducing dimensionality by focusing on the most relevant variables is a standard practice in data analysis. This dual ability to reduce data in both dimensions makes the statement false.

Data reduction is a key statistical technique that refers to the process of reducing the volume of data while maintaining its integrity and usefulness for analysis. This technique can be applied to both rows (observations) and columns (variables) in a dataset.

In the context of rows, data reduction often involves removing redundant or irrelevant observations or summarizing data points, such as through aggregation. When it comes to columns, data reduction may include techniques like feature selection, which eliminates unimportant variables, or dimensionality reduction methods such as Principal Component Analysis (PCA), which transforms a large number of variables into a smaller set of uncorrelated variables.

The statement that data reduction can only be applied to rows and not to columns is incorrect, as reducing dimensionality by focusing on the most relevant variables is a standard practice in data analysis. This dual ability to reduce data in both dimensions makes the statement false.