인류의 복지와 편익을 위한 인프라 건설을 주도하는토목공학과
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(2학년) 빅데이터분석기초 기말평가 주요 코딩_(박영훈 교수)
작성일
2023.06.01
작성자
부천대학교 토목공학과
#1번
cars=read.csv("sonata.csv", header=T, fileEncoding = "euc-kr")
head(cars)
summary(lm(제동거리~속도+0, data=cars))
#2번
data=read.csv("bucheon_company.csv",header=T, fileEncoding = "euc-kr")
head(data)
summary(lm(salary~ ., data=data))
model=lm(salary~ ., data=data)
predict(model,data.frame("experience"=c(9,8),"score"=c(85,90)), interval = "confidence")
summary(lm(experience~ ., data=data))
model=lm(experience~ ., data=data)
predict(model,data.frame("salary"=c(45,52),"score"=c(85,90)), interval = "confidence")
#3번
data=read.csv("corona.csv", header=T, fileEncoding = "euc-kr")
head(data)
data$Treat=as.factor(data$Treat)
data$Treat=relevel(data$Treat, ref="Cont")
summary(data)
out=lm(Postwt-Prewt~Prewt+Treat, data)
anova(out)
install.packages("multcomp")
library(multcomp)
dunnett=glht(out, linfct=mcp(Treat="Dunnett"))
summary(dunnett)
#4번
turkey=read.csv("turkey.csv", header=T, fileEncoding = "euc-kr")
head(turkey)
turkey=na.omit(turkey)
head(turkey)
install.packages("MASS")
library(MASS)
model1=lda(TYPE~HUM+ULN, data=turkey)
model1
predict(model1, data.frame("HUM"=c(135,140), "ULN"=c(140,145)))
model2=qda(TYPE~HUM+ULN, data=turkey)
model2
predict(model2, data.frame("HUM"=c(135,140), "ULN"=c(140,145)))
model3=lda(TYPE~TIN+CAR, data=turkey)
model3
predict(model3, data.frame("TIN"=c(150,160), "CAR"=c(790,810)))
model4=qda(TYPE~TIN+CAR, data=turkey)
model4
predict(model4, data.frame("TIN"=c(150,160), "CAR"=c(790,810)))