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


attach(delivery_time)

windows()
plot(delivery_time)

cor(Delivery.Time, Sorting.Time)

## [1] 0.8259973

summary(delivery_time)

##  Delivery.Time    Sorting.Time  
##  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.   :10.00

boxplot(delivery_time$Delivery.Time)

boxplot(delivery_time$Sorting.Time)

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

## 
## Call:
## lm(formula = Delivery.Time ~ Sorting.Time, data = delivery_time)
## 
## Coefficients:
##  (Intercept)  Sorting.Time  
##        6.583         1.649

summary(reg)

## 
## Call:
## lm(formula = Delivery.Time ~ Sorting.Time, data = delivery_time)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1729 -2.0298 -0.0298  0.8741  6.6722 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    6.5827     1.7217   3.823  0.00115 ** 
## Sorting.Time   1.6490     0.2582   6.387 3.98e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.935 on 19 degrees of freedom
## Multiple R-squared:  0.6823, Adjusted R-squared:  0.6655 
## F-statistic:  40.8 on 1 and 19 DF,  p-value: 3.983e-06

transform techniges

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

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

reg2<- lm(Delivery.Time ~ sqrt(Sorting.Time)) reg2 summary(reg2) 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 plot(reglog) cor(reglog) summary(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)

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)))

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) ```