The aim of this project is to analyse the spatial distribution of public transport stops in central Warsaw. The study focuses on the relationship between stop density, spatial accessibility and the simplification of stop data through 200-metre same-mode clustering.

Dokumen ini menyajikan materi Statistika Matematika secara komprehensif, mencakup konsep dasar statistika deskriptif dan inferensia, teori peluang, distribusi diskret dan kontinu, sebaran percontohan, pendugaan parameter, hingga pengujian hipotesis. Setiap topik dilengkapi dengan penurunan rumus secara matematis, contoh soal terstruktur, kode R yang dapat direplikasi, serta visualisasi data menggunakan ggplot2. Cocok digunakan sebagai referensi belajar mandiri maupun pendamping perkuliahan statistika tingkat menengah. Topik: Kaidah Peluang · Dalil Bayes · Distribusi Binomial, Poisson, Normal · Central Limit Theorem · Selang Kepercayaan · Uji Hipotesis

Simple Shiny application for next word prediction created in RStudio.

Dokumen ini menyajikan analisis statistika komprehensif data pernikahan dan perceraian di Jawa Barat periode 2021–2023, mencakup statistika deskriptif, kaidah peluang, distribusi probabilitas, simulasi CLT, hingga uji hipotesis berbasis data publik.

Session 3 tutorial code of the course "Introduction to Machine Learning in the Social Sciences" by Adrian Stanciu & Erik Paessler, University of Luxembourg, FHSE, 25/26

Session 4 tutorial code of the course "Introduction to Machine Learning in the Social Sciences" by Adrian Stanciu & Erik Paessler, University of Luxembourg, FHSE, 25/26

# 1. Simple Exponential Smoothing model_simple <- ets(hujan_ts, model = "ANN") # 2. Holt's Linear Trend model_holt <- ets(hujan_ts, model = "AAN") # 3. Brown's Linear Trend model_brown <- holt(hujan_ts, exponential = FALSE, damped = FALSE) # 4. Damped Trend model_damped <- ets(hujan_ts, model = "AAN", damped = TRUE) # 5. Simple Seasonal model_seasonal <- ets(hujan_ts, model = "ANA") # 6. Winters Additive model_win_add <- ets(hujan_ts, model = "AAA") # 7. Winters Multiplicative # Tidak boleh ada nilai 0 hujan_baru <- hujan_ts + 1 model_win_mult <- ets(hujan_baru, model = "MAM")

This project analyzes Electric Vehicle (EV) adoption trends in India using R and data visualization techniques. The report contains eight data-driven visualizations, including line charts, bar charts, pie charts, heatmaps, box plots, scatter plots, stacked bar charts, and an interactive Plotly visualization. The analysis explores EV registration growth, state-wise adoption patterns, vehicle category distribution, charging infrastructure relationships, and overall trends in the EV ecosystem.

Vedat Erdem Özkul