This project examines how much of a stock’s return variability can be statistically attributed to different market and macroeconomic factors. Using daily data over a five-year period retrieved via Yahoo Finance (yfinance), the study models stock returns as a function of explanatory variables such as market indices and commodity prices. The analysis focuses on partitioning total variance into orthogonal components to determine the proportion of variance each factor contributes to the stock’s overall volatility.

Project 2 Dataset 1: Movie Survey Data

Replication Report

This analysis of the Exercise Schedule & Meal Plan dataset revealed several key insights into fitness patterns across gender, BMI categories, and fitness goals. Mehreen Ali Gillani

La siguiente es la **Guía 1: Las Casas de Boston – Descubriendo la Regresión Lineal en GitHub Codespaces con Copilot**, adaptada a tu flujo de trabajo con Python, Streamlit y la integración de Copilot en el IDE de VsCode dentro de Codespaces.

women_weight <- c(38.9, 61.2, 73.3, 21.8, 63.4, 64.6, 48.4, 48.8, 48.5) men_weight <- c(67.8, 60, 63.4, 76, 89.4, 73.3, 67.3, 61.3, 62.4) gender_weight = data.frame( gender =rep(c("men", "women") , each = 9) , weight = c(men_weight , women_weight) ) gender_weight%>% group_by(gender)%>% summarise( count = n() , mean(weight) , sd(weight) ) ggboxplot(data = gender_weight , x = "gender" ,y = "weight" , color = "gender" ,palette =c( "#E69F00", "#56B4E9") , title = "Is weight differance" ,xlab = "Gender" , ylab ="weight" ) with(gender_weight ,shapiro.test(weight[gender == "men"]) ) with(gender_weight ,shapiro.test(weight[gender == "women"]) ) t.test(weight ~ gender , data = gender_weight , var.equal = T )

Project 2's Second Dataset: Tidying Vaccine Data