文档介绍：R语言中的ttest和ANOVAIndexT-testANOVA 单因子方差分析 同时置信区间 双因子方差分析 有交互作用的方差分析 多重t-test分析Vasishth’sHeightExampleSAMP={,,,,,,,,,,}Shrinkingdrug(non-effectvalue=64)大部分情况下我们并不知道σ——T分布>pt(-,df=10)+(1-pt(,df=10))[1]’sHeightExampleThep-valueofthistwo-sidedt-.>samp<-c(,,,,,,+,,,,)>(samp,mu=64)OneSamplet-testdata:sampt=-,df=10,p-value=:truemeanisnotequalto6495percentconfidenceinterval::>source(file="")>(,tail="both",df=10)曲线下小于-%通过KeithJohnson’()(x,y=NULL,alternative=c("","less","greater"),mu=0,paired=FALSE,=FALSE,=,...)若仅出现数据x,则进行单样本t检验;若出现数据x和y,则进行二样本的t检验alternative=c("","less","greater")用于指定所求置信区间的类型;alternative=""是缺省值,表示求置信区间alternative="less"表示求置信上限;alternative="greater",它仅在假设检验中起作用,>x<-c(175,176,173,175,174,173,173,176,173,179)>(x)OneSamplet-testdata:xt=,df=9,p-value<-16alternativehypothesis:truemeanisnotequalto095percentconfidenceinterval::>(x)$#置信区间[1](,"")[1]>x<-c(,,,,,,,)>y<-c(,,,,,,)>(x,y,=TRUE)TwoSamplet-testdata:xandyt=-,df=13,p-value=:truedifferenceinmeansisnotequalto095percentconfidenceinterval:-:>x<-c(,,,,,,,)>y<-c(,,,,,,,)>(x,y,paired=TRUE)Pairedt-testdata:xandyt=,df=7,p-value=:truedifferenceinmeansisnotequalto095percentconfidenceinterva