Forecasting methods are critical tools used to predict future trends, enabling better business decisions by analyzing past and present data. These methods range from qualitative techniques based on expert judgments to quantitative models that leverage statistical analysis and machine learning algorithms.
Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.
Causal models are frameworks used to represent and analyze the cause-and-effect relationships between variables, providing a structured approach to understanding how changes in one variable can influence others. They are essential in fields like epidemiology, economics, and machine learning for making predictions and informed decisions based on causal inference rather than mere correlation.
Enrollment projections are estimates of future student enrollment numbers, crucial for educational institutions to plan resources, staffing, and budgeting effectively. These projections rely on historical data, demographic trends, and policy changes to predict shifts in student populations over time.