Problem Definition and Data Collection: Clearly identify the problem to be studied and collect relevant data.
Data Exploration and Preparation: Includes cleaning, transforming, and initially exploring the data to understand its characteristics and relationships.
Model Selection: Based on prior knowledge and the nature of the data, an appropriate statistical model is chosen.
Parameter Estimation: Use estimation methods to calculate the model’s parameters.
Model Verification and Validation: Check the quality of the model through various tests and diagnostic methods.
Interpretation of Results: Interpret the results of the model in the context of the study problem.
Use of the Model for Prediction or Inference: Apply the model to make predictions about new data or to make inferences about the studied population.
Model