فلش‌کارت‌های آموزشی کالیبراسیون و اعتبارسنجی مدل‌های آماری در کشاورزی

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره کالیبراسیون و اعتبارسنجی مدل‌های آماری در کشاورزی

موضوع کلی: امنیت غذایی و کشاورزی پایدار

موضوع میانی: تحلیل داده‌های کشاورزی با استفاده از نرم‌افزارهای آماری

📋 سرفصل‌های دوره

• 1. Introduction to statistical modeling in agricultural research
• 2. Understanding data types and measurement scales in agriculture
• 3. Descriptive statistics and preliminary data exploration
• 4. Probability distributions relevant to agricultural data
• 5. Testing distributional assumptions: Shapiro-Wilk and Kolmogorov-Smirnov
• 6. Parameter estimation: method of moments and maximum likelihood
• 7. Introduction to model calibration frameworks
• 8. Calibration of linear regression models for crop yield prediction
• 9. Non-linear model calibration: exponential and logarithmic forms
• 10. Goodness-of-fit metrics: R2, adjusted R2, AIC, BIC
• 11. Cross-validation techniques: k-fold and leave-one-out
• 12. Resampling methods: bootstrap for model validation
• 13. Validation of predictive models: holdout and temporal splitting
• 14. Error analysis: RMSE, MAE, and MAPE in agricultural contexts
• 15. Checking model assumptions: homoscedasticity and independence
• 16. Addressing overdispersion in count data models
• 17. Generalized linear models (GLM) calibration for binary outcomes
• 18. Validation of GLMs: deviance and residual analysis
• 19. Spatial statistical models: variogram calibration and kriging
• 20. Time series calibration: trend decomposition and seasonal adjustment
• 21. Model selection criteria: AICc and BIC in small samples
• 22. Calibration of mixed-effects models for hierarchical agricultural data
• 23. Validation of mixed models: marginal and conditional R2
• 24. Bayesian model calibration: priors and posterior estimation
• 25. Markov Chain Monte Carlo (MCMC) for model validation
• 26. Posterior predictive checks and model adequacy
• 27. Calibration of machine learning models: random forests in agriculture
• 28. Validation of ML models: out-of-bag error and cross-validation
• 29. Feature selection and variable importance in agricultural modeling
• 30. Calibration of survival analysis models for crop failure time
• 31. Validation of survival models: Kaplan-Meier and Cox proportional hazards
• 32. Handling missing data: imputation techniques for agricultural datasets
• 33. Sensitivity analysis in model calibration and validation
• 34. Uncertainty quantification: confidence and prediction intervals
• 35. Reporting calibrated models: tables, graphs, and interpretation
• 36. Case study: calibrating a yield prediction model using R
• 37. Case study: validating a climate impact model using Python
• 38. Ethical considerations and data privacy in agricultural analytics
• 39. Best practices for reproducible research in agricultural statistics
• 40. Advanced topics: Gaussian process calibration and emulation
• 41. Calibration of network models for pest spread prediction
• 42. Validation of network models: simulation and real-world comparison
• 43. Calibration of stochastic models: Monte Carlo and agent-based approaches
• 44. Validation of stochastic models: confidence regions and scenario analysis
• 45. Integrating remote sensing data with statistical model calibration
• 46. Workflow automation: from data import to model validation scripts
• 47. Common pitfalls in agricultural model calibration and how to avoid them
• 48. Final project: design and validate a complete statistical model for a crop system