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insurance <- read.csv("insurance.csv", stringsAsFactors = TRUE)str(insurance)#既然因变量是charges,我们就来看一下它是如何分布的summary(insurance$charges)hist(insurance$charges)table(insurance$region)cor(insurance[c("age","bmi","children","charges")])pairs(insurance[c("age","bmi","children","charges")])library("psych")pairs.panels(insurance[c("age","bmi","children","charges")])#lm代表线性回归算法ins_model <- lm(charges ~ age + children + bmi + sex + smoker + region, data=insurance)ins_model <- lm(charges ~ . , data=insurance)ins_modelsummary(ins_model)insurance$age2 <- insurance$age^2insurance$bmi30 <- ifelse(insurance$bmi >= 30, 1, 0)ins_models <- lm(charges ~ age + age2 + children + bmi + sex + bmi30*smoker + region , data=insurance)summary(ins_models)