Visualisasi Data Scatter Plot BAB 3 pada Buku Belajar Statistika dengan R (Pertemuan ke-2 Komputasi Statistika) - Shabrina Zuhratul Amaliah (2507016006)

Pertemuan ke-2 Komputasi Statistika Shabrina Zuhratul Amaliah (2507016006)

This academic consulting report was developed for FINLYTS coursework and presents a machine learning-based financial forecasting framework for Bitcoin (BTC-USD). The report demonstrates the complete analytical workflow, including data documentation, data preparation, feature engineering, forecasting model development, model evaluation, explainable artificial intelligence (SHAP analysis), and investment recommendation. This document is prepared for educational and academic purposes only.

Phân tích Kaplan-Meier, hồi quy Cox, BMA, Bootstrap, Calibration, LASSO và trực quan hóa 3D xác suất tái phạm sử dụng dữ liệu Rossi.

Qissya Nasuha Amalia (2507016034) BAB 3:Visualisasi Data

Tugas pertemuan ke 2 komputasi statistika

A comprehensive, beginner-friendly walkthrough demonstrating how to implement Bayesian spatial disease mapping using R-INLA (Integrated Nested Laplace Approximations). This tutorial guides users through the entire epidemiological data pipeline: from importing local administrative shapefiles (using Kenya as a study case) to simulating realistic disease data using Poisson distributions and accounting for background populations. It explicitly breaks down how to construct spatial neighborhood graphs and configure the classic Besag-York-Mollié (BYM) model to split geographic variation into smooth regional patterns and localized noise. Finally, it demonstrates how to extract posterior mean relative risks and visualize public health hot-spots using advanced continuous color styling in ggplot2. Ideal for epidemiologists, data scientists, and students transitioning into spatial statistics.