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