simple linear regression

```{}

delivery_time <- read.csv(“D:/excelR/regression assignment/Simple Linear Regression/delivery_time.csv”) View(delivery_time)

attach(delivery_time)

plot(delivery_time) cor(Delivery.Time, Sorting.Time) summary(delivery_time) boxplot(delivery_time\(Delivery.Time) boxplot(delivery_time\)Sorting.Time)

reg <- lm(Delivery.Time ~ Sorting.Time, data = delivery_time) reg summary(reg)

transform techniges

sqrt on y

reg1<- lm(sqrt(Delivery.Time) ~ Sorting.Time)

reg1 summary(reg1) confint(reg,level = 0.95) predict(reg,interval =“prediction”)

plot(reg1) cor(sqrt(Delivery.Time), Sorting.Time) plot(sqrt(Delivery.Time, Sorting.Time))

sqrt on x

reg2<- lm(Delivery.Time ~ sqrt(Sorting.Time)) reg2 summary(reg2) confint(reg,level = 0.95) predict(reg,interval =“prediction”)

plot(reg2)

cor(reg2)

plot(delivery_time\(log(Delivery.Time, delivery_time\)I(Sorting.TimeSorting.Time) data=delivery_time) #cor(log(Delivery.Time), I(Sorting.TimeSorting.Time));

reg3<-lm(log(Delivery.Time)~Sorting.Time + I(Sorting.Time*Sorting.Time), data=delivery_time) reg3 summary(reg3) confint(reg3,level=0.95) predict(reg3,interval=“predict”)

reglog<- lm(log(Delivery.Time) ~ Sorting.Time) reglog summary(reglog) plot(reglog) cor(reglog)

reglogx<- lm(Delivery.Time ~ log(Sorting.Time)) reglogx summary(reglogx) cor(Delivery.Time, log(Sorting.Time)) confint(reglogx, level = 0.95) predict(reglogx, interval = “predict”)

regxy<- lm(log(Delivery.Time) ~ log(Sorting.Time)) regxy summary(regxy)
cor(log(Delivery.Time), log(Sorting.Time)) plot(log(Delivery.Time), log(Sorting.Time), data = delivery_time)

reg22<- lm((Delivery.Time^2) ~ Sorting.Time) reg22 summary(reg22)

reg23<- lm(Delivery.Time ~ (Sorting.Time)^2) reg23
summary(reg23)

reg24<- lm((Delivery.Time^2) ~ (Sorting.Time)^2) reg24 summary(reg24)

reg25<- lm (log(Delivery.Time) ~ (Sorting.Time)) reg25 summary(reg25)

reg26<- lm(log(log(Delivery.Time)) ~ (Sorting.Time)) reg26
summary(reg26)

cor(log(Delivery.TimeDelivery.Time), (Sorting.TimeSorting.Time))

reg31 <- lm(log(Delivery.TimeDelivery.Time) ~ (Sorting.TimeSorting.Time)) reg31
summary(reg31)

reg32<- lm(log(Delivery.TimeDelivery.Time) ~ (Sorting.TimeSorting.Time)) reg32
summary(reg32)

log(log)xx ~ log(log)yy

reg28<- lm(log(log(Delivery.TimeDelivery.Time)) ~ (log(Sorting.TimeSorting.Time))) reg28 summary(reg28) cor(log(log(Delivery.TimeDelivery.Time)), (log(Sorting.TimeSorting.Time))) plot(log(log(Delivery.TimeDelivery.Time)), (log(Sorting.TimeSorting.Time)))

best model

reg3<-lm(log(Delivery.Time)~Sorting.Time + I(Sorting.Time*Sorting.Time), data=delivery_time) reg3 summary(reg3) confint(reg3,level=0.95) predict(reg3,interval=“predict”) # Multiple R-squared: 0.7649, Adjusted R-squared: 0.7387