Análisis del impacto de eventos climáticos severos en salud y economía en EE.UU como parte del curso de investigación reproducible en Coursera

倪淑妍 2220232050

4_DBCA_Gemini

The objective of this project is to analyze the impact of predictor collinearity on the stability and statistical properties of OLS estimators and the shape of the RSS function.

Develop a comprehensive and practical guide titled “Mastering the Art of AI Prompting.” The guide should be structured, detailed, and suitable for beginners through advanced users. It must include the following components

The aim of this project is look at various ways to accurately predict measurable minerals used in treatment of drinking water. sample drinking water was tested and recorded for 10 minerals. "Calcium.CA" "magnesium.Mg" "Sodium.Na" "Potassium" "Sulfates" "Chlorides" "Nitrates" "Nitrites" "dry.residues" "Bicarbonates" ##Task !developed a classification model to identify ideal mineral composition for water treatment using both WHO standards and data-driven approaches. While WHO guidelines served as the foundation benchmark, the decision tree and random forest models provided refined thresholds tailored to the data set. we shall be comparing the record with WHO standard to determine if those mineral element are present at recommended amount or in excess in our drinking water. we shall train our model to assist us to easily predict the recommended amount of mineral required.

Extends the analysis with non-parametric and semi-parametric methods — Kaplan–Meier estimation, Nelson–Aalen cumulative hazard, formal hypothesis testing, full Cox regression with diagnostics, model refinement for PH violations, and a comprehensive synthesis integrating all three analytical frameworks.