Final answer:
An analyst in this phase works on predictive modeling, using statistical models to forecast future events and validate these models through cross-validation for accuracy. Examples include stock market predictions and weather forecasts.
Step-by-step explanation:
In the phase described, an analyst deals with predictive modeling activities, which include steps like estimating future values, extending correlations found in Exploratory Data Analysis (EDA) to mathematical models, predicting outcomes based on input values, and conducting cross-validation to ensure the accuracy of predictive models.
For instance, an economist predicting stock market trends would create a model to estimate likely index points and compare these predictions with actual outcomes at the close of each trading day. This process involves using statistical and computational tools to create reliable statistical models that are central to forecasting and decision-making across various fields including finance, engineering, and environmental science.
Good predictive models are essential for providing actionable insights and making informed decisions. They must factor in the uncertainty inherent in real-world scenarios, much like meteorologists use models to provide probabilistic weather forecasts. The application of mathematical methods, such as linear regression and correlation analysis, enables analysts to predict future outcomes based on historical and current data, adjust their models accordingly, and develop strategies to approach future events.