图片发自简书App

图片发自简书App

#参数估计

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检验

data<-read.csv("G:/数据分析/五、r/R基础课件/house_price_gr.csv")

View(data)

t.test(data$rate,mu=0.1)

t.test(data$rate,mu=0.1,alternative = "greater")

#两样本T检验

data<-read.csv("G:/数据分析/五、r/R基础课件/creditcard_exp.csv")

View(data)

tapply(data$avg_exp,data$gender,summary)#看均值差别

var.test(data$avg_exp~data$gender)#方差齐性检验

t.test(data$avg_exp~data$gender,var.equal=T)#两样本T检验

#单因素方差分析

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)#方差齐性检验