Final answer:
During the Data Analysis stage in predictive modeling, the key task is to select the input data for the model. This involves careful evaluation of data quality, relevance, preprocessing, and potentially visualizing relationships between variables.
Step-by-step explanation:
When building a predictive model, the Data Analysis stage is where you select the input data. This stage involves identifying which data will be used as input variables (often referred to as features or predictors) for the model. You assess the quality and relevance of your data, and decide on which variables might be potential predictors for your outcome of interest. Moreover, it may also involve preprocessing and cleaning the data to ensure it is suitable for the modeling process.
In a broader sense, data analysis can encompass more tasks such as visualizing the data, assessing any relationship between variables, and selecting the proper statistical methods for analysis. This could involve creating scatter plots to visualize relationships, calculating least-squares lines and correlation coefficients, and making predictions based on the model's outcomes.