Which technique is commonly used for data analysis in the exam?

Regression analysis is widely utilized in data analysis to explore and quantify relationships between variables. This technique helps in predicting the value of a dependent variable based on one or more independent variables, enabling you to understand how changes in inputs can affect outcomes. It is often employed in various fields, such as economics, biology, and social sciences, due to its effectiveness in establishing predictive models and assessing trends within datasets.

In contexts like exams or academic assessments, regression analysis can be particularly valuable for providing insights into data that involve cause-and-effect relationships. This capability makes it an essential tool for data-driven decision-making and interpretation.

Other techniques, while also valuable, serve different purposes. Descriptive statistics focuses on summarizing and describing the characteristics of a dataset, time series analysis looks at data points collected or recorded at successive points in time to identify trends, and cluster analysis groups similar data points together without predefined labels. Each of these methods has its strengths, but regression analysis is often preferred for its predictive capabilities in analyzing the relationships between variables.