A comprehensive collection of premium prompts for advanced statistical analysis, covering Bayesian inference, time series forecasting, causal inference, survival analysis, and hierarchical modeling.
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Outline a survival analysis approach to model customer churn, including data requirements, common models (e.g., Kaplan-Meier, Cox Proportional Hazards), and interpretation of results.
Design a causal inference study to evaluate the impact of a policy intervention, outlining potential methods (e.g., Difference-in-Differences, Regression Discontinuity) and assumptions.
Formulate a hierarchical (multilevel) statistical model for data with nested structures, detailing model specification, interpretation of fixed and random effects, and assumptions.
Generate a Bayesian framework for analyzing A/B test results, providing posterior distributions for conversion rates and direct probabilities of one variant outperforming another.
Develop a detailed time series forecasting model using ARIMA, including data preparation steps, model selection criteria, and evaluation metrics.
This premium pack provides a curated collection of advanced prompts for statistical modeling, covering robust regression, A/B testing design, time series forecasting, and customer segmentation. Leverage these prompts to extract deeper insights, make data-driven decisions, and enhance your analytical capabilities.
A comprehensive toolkit of prompts for advanced statistical modeling, covering A/B testing, regression, time series, and experimental design. Leverage these prompts to perform rigorous data analysis, make accurate predictions, and design robust research studies.
