Which of the following techniques reduces the number of random variables under consideration in data analysis?

Dimensionality reduction is a key technique in data analysis that focuses on decreasing the number of random variables under consideration. This is particularly important in datasets where the number of features (dimensions) can lead to complexity and difficulties in visualization, interpretation, and computations. By reducing dimensions, the technique retains the most crucial information while eliminating redundant or irrelevant features, which can help in improving the performance of machine learning algorithms and making the analysis more manageable.

In contrast, while compression can reduce the size of data for storage or transmission, it does not necessarily reduce the number of variables being analyzed. Visualization aids in interpreting data but does not alter the underlying dimensions of the dataset. Summarization condenses data into a more digestible format but also does not inherently reduce the number of variables being studied. Therefore, dimensionality reduction is the most suitable answer as it specifically addresses the need to limit the number of variables while preserving essential information for effective analysis.