This R script performs a comprehensive statistical and machine learning analysis of a health and fitness dataset. The Health and Fitness dataset can be accessed in the following link: Kaggle. It begins with descriptive statistics of key variables, then explores relationships between features. Specifically, it runs a regression model predicting Calories_Burned based on BMI, Workout_Frequency, and Workout_Type. The code also calculates correlations, including the relationship between Water_Intake and Avg_BPM, and between Workout_Type and Calories_Burned, to assess how hydration and exercise style influence performance. To uncover patterns in member behavior, it applies K-means clustering on variables such as Age, Avg_BPM, and Calories_Burned, grouping individuals into distinct activity level categories. Additionally, the script visualizes the distributions of calories and exercise types, highlighting measures of central tendency along with the 25th and 75th percentiles. Together, this analysis provides insights into how workout habits, body metrics, and exercise choices shape overall fitness outcomes.

《因果效应：研究设计和因果关系导论》

Providing access on public domain for retrieving the files a) HTML b) RMD c) CSV files

5003221137 Nabila Athira 5003221183 Ashila Nasywa 5003221184 Aisha Zahra 5003221190 Ave Regina

5003221137 Nabila Athira 5003221183 Ashila Nasywa 5003221184 Aisha Zahra 5003221190 Ave Regina

An R-based geospatial analysis workflow for mapping and monitoring ash dieback disease (Hymenoscyphus fraxineus) impacts on forest stands. This repository contains reproducible code for processing field survey data, creating stem distribution maps, and analyzing spatial patterns of disease progression in ash tree populations. The tool integrates forest inventory methods with spatial analysis techniques to support forest health monitoring and management decisions in response to this invasive fungal pathogen affecting European ash forests.