O.V

Базовый анализ данных в R: dplyr + ggplot2 (датасет mtcars)

ToR A: Assessment of Conservation and Technical Measures for Barents Sea Fish Stock Management

# Exponential Smoothing Analysis: Time Series Forecasting Study This comprehensive analysis explores exponential smoothing methods for forecasting time series data across multiple datasets including Australian livestock, Botswana exports, Chinese GDP, Australian gas production, and retail sales. The study systematically compares simple exponential smoothing (ETS(A,N,N)) with trend-based models (ETS(A,A,N)) and damped trend variants (ETS(A,Ad,N)), evaluating their performance through metrics like RMSE, AIC, and BIC while examining when multiplicative seasonality outperforms additive approaches. Key findings demonstrate that multiplicative seasonality is essential for data with proportionally growing variance, damped trends provide more conservative long-term forecasts though not always better statistical fit, and STL decomposition with Box-Cox transformation can improve forecast accuracy for complex seasonal patterns. The analysis includes detailed residual diagnostics, prediction interval calculations, and test set validation to determine which forecasting methods best balance accuracy and practical applicability for different types of time series data.

DerekCorcoran_WhereandHowtoTransform

Alejandra's replication projects for Experimental Methods class.

This project demonstrates how to transform wide datasets into tidy formats using R. Three datasets—Sales, Scores, and Vaccinations—were cleaned, reshaped, and summarized to prepare them for analysis and visualization. The project highlights the use of pivot_longer(), mutate(), and group_by() for data tidying, and includes visual summaries created with ggplot2. The completed outputs were exported to CSV files and packaged into a single zip file for easy sharing.

CRD/One-Way ANOVA and Multiple Comparisons in R