#參數(shù)估計
data<-read.csv("house_price_gr.csv")
View(data)
mean(data$rate)
a<-sd(data$rate)
a
b<-a/sqrt(nrow(data))
b
alpha<-0.95
qnorm((1+alpha)/2)
qt((1+alpha)/2,nrow(data)-1)
xbar<-mean(data$rate)
xbar-qnorm((1+alpha)/2)*b
xbar+qnorm((1+alpha)/2)*b
xbar+qnorm((1+0.99)/2)*b
xbar-qnorm((1+0.99)/2)*b
#T檢驗
data<-read.csv("G:/數(shù)據(jù)分析/五、r/R基礎(chǔ)課件/house_price_gr.csv")
View(data)
t.test(data$rate,mu=0.1)
t.test(data$rate,mu=0.1,alternative = "greater")
#兩樣本T檢驗
data<-read.csv("G:/數(shù)據(jù)分析/五、r/R基礎(chǔ)課件/creditcard_exp.csv")
View(data)
tapply(data$avg_exp,data$gender,summary)#看均值差別
var.test(data$avg_exp~data$gender)#方差齊性檢驗
t.test(data$avg_exp~data$gender,var.equal=T)#兩樣本T檢驗
#單因素方差分析
oneway.test(avg_exp~edu_class,data,var.equal=F)
anova(lm(avg_exp~edu_class))
library(car)
bartlett.test(avg_exp~edu_class,data)#方差齊性檢驗
