Group assignment

Most hospitals have a 30-day mortality rate between 14 and 16, as indicated by the highest bars in the central portion of the chart. This suggests that these rates are the most common among hospitals, forming a bell-shaped distribution. The data also shows a slight right skew, indicating that while most hospitals fall within the central range, a few have significantly higher rates, suggesting variability in care quality. Outliers on both ends of the histogram reveal hospitals with either exceptionally low or high mortality rates, highlighting potential areas for further investigation or improvement in healthcare outcomes. The data comes from the Hospital Compare website (http://hospitalcompare.hhs.gov) run by the U.S. Department of Health and Human Services

RIMA Musiman atau Seasonal Autoregressive Integrated Moving Average (SARIMA) merupakan pengembangan dari model Autoregressive Integrated Moving Average (ARIMA) pada data deret waktu yang memiliki pola musiman. Metode ini dipopulerkan oleh George Box dan Gwilym Jenskins sekitar tahun 1970-an.

I prompted ChatGPT to generate datasets containing three tables: Borrowers, Loans, and Market Risks. The goal was to create a realistic data structure for analyzing lending and market risk factors, enabling me to assess relationships between borrower profiles, loan characteristics, and market conditions.

Pipe operators like %>% have revolutionized my data processing in R, making my code cleaner and more intuitive. They enable straightforward, left-to-right chaining of operations, enhancing readability and efficiency. From converting factors to numeric values and handling side effects with %T>%, to seamlessly transitioning between data manipulation and visualization with dplyr and ggplot2, these operators have streamlined my workflow. The use of placeholders, functional sequences, and compound assignment with %<>% has further simplified repetitive tasks, allowing me to focus more on analyzing the data itself.