This project explores the global impact of COVID‑19 using Kaggle datasets and the R programming language. It applies data cleaning and visualization techniques to highlight key insights. The analysis includes: A line chart showing the worldwide trend of confirmed cases over time. A bar chart comparing the top 10 countries most affected by confirmed cases. A scatter plot examining the relationship between recoveries and deaths across countries. A stacked bar chart providing a detailed breakdown of confirmed, deaths, and recovered cases for India and the US.

This document continues the introduction to regression analysis using a real dataset on homework deadlines and student performance. It focuses on interpreting regression results, evaluating the strength of relationships using R^2, testing for statistical significance using the F-test, examining the ANOVA table, and using the fitted regression model to make predictions.

Visualisasi data merupakan cara penting dalam analisis data untuk melihat pola, tren, dan hubungan dalam suatu dataset. Salah satu jenis visualisasi yang sering digunakan ketika data memiliki unsur waktu adalah time series. Dataset yang digunakan adalah Bike Sharing Dataset, yang berisi informasi jumlah penyewaan sepeda harian beserta beberapa variabel lain seperti cuaca, musim, dan jenis pengguna. Melalui visualisasi time series, kita dapat melihat bagaimana jumlah penyewaan sepeda berubah dari waktu ke waktu.

An introductory tutorial on simple linear regression in R using a real dataset on homework time and student performance. The document covers scatterplots, regression modelling, residuals, R^2, hypothesis testing, and prediction.