a<-read.csv("E:\\Data science\\Practies\\expenditure.csv")
View(a)
attach(a)
mean(ship)
## [1] 1.17966
mean(exp)
## [1] 2.70074
median(ship)
## [1] 1.1655
median(exp)
## [1] 2.7235
sd(exp)
## [1] 0.1969706
sd(ship)
## [1] 0.05311274
var(exp)
## [1] 0.03879742
var(ship)
## [1] 0.002820964
library(moments)
skewness(a)
## ship exp
## 0.2936755 -0.2234026
kurtosis(a)
## ship exp
## 2.773710 1.593454
hist(ship)
hist(exp)
barplot(exp)
barplot(ship)
boxplot(exp)
boxplot(ship)
cor(ship,exp)
## [1] 0.7582614
plot(ship,exp)
m1<-lm(exp~ship,data=a)
summary(m1)
##
## Call:
## lm(formula = exp ~ ship, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.216952 -0.101899 -0.002395 0.090965 0.308161
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.6165 0.4121 -1.496 0.141
## ship 2.8120 0.3490 8.058 1.8e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1297 on 48 degrees of freedom
## Multiple R-squared: 0.575, Adjusted R-squared: 0.5661
## F-statistic: 64.93 on 1 and 48 DF, p-value: 1.8e-10
m2<-lm(exp~log(ship),data=a)
summary(m2)
##
## Call:
## lm(formula = exp ~ log(ship), data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.219732 -0.100972 -0.004189 0.088629 0.307779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.14983 0.06969 30.848 < 2e-16 ***
## log(ship) 3.35429 0.40964 8.188 1.14e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1285 on 48 degrees of freedom
## Multiple R-squared: 0.5828, Adjusted R-squared: 0.5741
## F-statistic: 67.05 on 1 and 48 DF, p-value: 1.145e-10
m3<-lm(exp~sqrt(ship),data=a)
summary(m3)
##
## Call:
## lm(formula = exp ~ sqrt(ship), data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.218321 -0.102346 -0.003258 0.089748 0.308015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.9721 0.8215 -4.835 1.41e-05 ***
## sqrt(ship) 6.1453 0.7563 8.125 1.43e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1291 on 48 degrees of freedom
## Multiple R-squared: 0.579, Adjusted R-squared: 0.5702
## F-statistic: 66.02 on 1 and 48 DF, p-value: 1.425e-10
m4<-lm(log(exp)~ship,data=a)
summary(m4)
##
## Call:
## lm(formula = log(exp) ~ ship, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08208 -0.03732 0.00068 0.03260 0.11462
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2543 0.1538 -1.653 0.105
## ship 1.0555 0.1302 8.104 1.53e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04842 on 48 degrees of freedom
## Multiple R-squared: 0.5777, Adjusted R-squared: 0.5689
## F-statistic: 65.68 on 1 and 48 DF, p-value: 1.534e-10
pv<-predict(m2)
pv
## 1 2 3 4 5 6 7 8
## 2.679337 2.707860 2.838762 2.744581 2.752998 2.685061 2.553846 2.595221
## 9 10 11 12 13 14 15 16
## 2.653457 2.653457 2.827818 2.827818 2.874085 2.874085 2.836029 2.836029
## 17 18 19 20 21 22 23 24
## 2.884879 2.884879 2.890263 2.890263 3.047894 3.047894 2.855111 2.855111
## 25 26 27 28 29 30 31 32
## 2.786458 2.786458 2.755799 2.562755 2.562755 2.621553 2.621553 2.633189
## 33 34 35 36 37 38 39 40
## 2.633189 2.627376 2.627376 2.609875 2.609875 2.641890 2.641890 2.547894
## 41 42 43 44 45 46 47 48
## 2.547894 2.656342 2.656342 2.670732 2.670732 2.529972 2.529972 2.407985
## 49 50
## 2.407985 2.392419
pv1<-as.data.frame(pv)
pv1
## pv
## 1 2.679337
## 2 2.707860
## 3 2.838762
## 4 2.744581
## 5 2.752998
## 6 2.685061
## 7 2.553846
## 8 2.595221
## 9 2.653457
## 10 2.653457
## 11 2.827818
## 12 2.827818
## 13 2.874085
## 14 2.874085
## 15 2.836029
## 16 2.836029
## 17 2.884879
## 18 2.884879
## 19 2.890263
## 20 2.890263
## 21 3.047894
## 22 3.047894
## 23 2.855111
## 24 2.855111
## 25 2.786458
## 26 2.786458
## 27 2.755799
## 28 2.562755
## 29 2.562755
## 30 2.621553
## 31 2.621553
## 32 2.633189
## 33 2.633189
## 34 2.627376
## 35 2.627376
## 36 2.609875
## 37 2.609875
## 38 2.641890
## 39 2.641890
## 40 2.547894
## 41 2.547894
## 42 2.656342
## 43 2.656342
## 44 2.670732
## 45 2.670732
## 46 2.529972
## 47 2.529972
## 48 2.407985
## 49 2.407985
## 50 2.392419