launch <- read.csv(“challenger2.csv”)
head(launch) str(launch) summary(launch)
model <- lm(distress_ct ~ temperature, data = launch) summary(model)
plot(launch\(temperature, launch\)distress_ct, xlab = “Temperature”, ylab = “Distress Count”, main = “Distress Count vs Temperature”)
abline(model, col = “red”)
model2 <- lm(distress_ct ~ temperature + field_check_pressure, data = launch) summary(model2)
In this activity, I recreated a linear regression analysis using the Challenger2 dataset. The results show that temperature has a negative relationship with distress count, meaning that lower temperatures are associated with a higher number of distress incidents.
The regression output helped me understand how temperature influences distress levels, and the model summary provided insights into coefficient significance and overall model fit. This activity reinforced my understanding of how linear regression can be used to analyze relationships between variables and interpret real-world data.