What type of analysis is utilized in predictive analytics to make decisions?

The correct choice emphasizes the use of historical and statistical analysis in predictive analytics. Predictive analytics relies heavily on data from past events and statistical models to forecast future outcomes. This process involves analyzing historical data to identify trends and patterns, which are then used to make informed decisions about what is likely to happen next.

Statistical techniques, such as regression analysis and time series analysis, are common in this field and help in quantifying the relationships between different variables. By understanding these relationships, organizations can develop models that predict future behavior or events, enabling them to take proactive measures rather than reactive ones.

In contrast, qualitative feedback analysis may provide insights based on subjective interpretations, but it doesn’t rely on structured data that can be statistically analyzed. Random event generation does not apply in this context, as it does not offer a systematic approach to forecasting based on past data. Common sense reasoning, while useful in many decision-making situations, is often subjective and lacks the rigor and reliability that statistical analysis provides, making it less effective for the objective nature of predictive analytics.