prediction of delivery time using sorting time
deltime=read.csv("E:\\Data science\\delivery_time.csv")
View(deltime)
summary(deltime)## Dt St
## Min. : 8.00 Min. : 2.00
## 1st Qu.:13.50 1st Qu.: 4.00
## Median :17.83 Median : 6.00
## Mean :16.79 Mean : 6.19
## 3rd Qu.:19.75 3rd Qu.: 8.00
## Max. :29.00 Max. :10.00attach(deltime)
qqnorm(Dt)
cor(St,Dt)## [1] 0.8259973plot(St,Dt)
windows()
m1<-lm("Dt~St",data=deltime)
m1##
## Call:
## lm(formula = "Dt~St", data = deltime)
##
## Coefficients:
## (Intercept) St
## 6.583 1.649pv<-predict(m1,deltime)
pv## 1 2 3 4 5 6 7
## 23.072933 13.178814 16.476853 21.423913 23.072933 16.476853 18.125873
## 8 9 10 11 12 13 14
## 11.529794 23.072933 21.423913 19.774893 13.178814 18.125873 11.529794
## 15 16 17 18 19 20 21
## 11.529794 13.178814 16.476853 18.125873 9.880774 18.125873 14.827833pread<-as.data.frame(pv)
pread## pv
## 1 23.072933
## 2 13.178814
## 3 16.476853
## 4 21.423913
## 5 23.072933
## 6 16.476853
## 7 18.125873
## 8 11.529794
## 9 23.072933
## 10 21.423913
## 11 19.774893
## 12 13.178814
## 13 18.125873
## 14 11.529794
## 15 11.529794
## 16 13.178814
## 17 16.476853
## 18 18.125873
## 19 9.880774
## 20 18.125873
## 21 14.827833final<-cbind(deltime,pv)
final## Dt St pv
## 1 21.00 10 23.072933
## 2 13.50 4 13.178814
## 3 19.75 6 16.476853
## 4 24.00 9 21.423913
## 5 29.00 10 23.072933
## 6 15.35 6 16.476853
## 7 19.00 7 18.125873
## 8 9.50 3 11.529794
## 9 17.90 10 23.072933
## 10 18.75 9 21.423913
## 11 19.83 8 19.774893
## 12 10.75 4 13.178814
## 13 16.68 7 18.125873
## 14 11.50 3 11.529794
## 15 12.03 3 11.529794
## 16 14.88 4 13.178814
## 17 13.75 6 16.476853
## 18 18.11 7 18.125873
## 19 8.00 2 9.880774
## 20 17.83 7 18.125873
## 21 21.50 5 14.827833