salary<-read.csv("E:\\Data science\\Salary_Data.csv")
View(salary)
dim(salary)
## [1] 30 2
attach(salary)
summary(salary)
## exp sal
## Min. : 1.100 Min. : 37731
## 1st Qu.: 3.200 1st Qu.: 56721
## Median : 4.700 Median : 65237
## Mean : 5.313 Mean : 76003
## 3rd Qu.: 7.700 3rd Qu.:100545
## Max. :10.500 Max. :122391
qqnorm(sal)
windows()
plot(exp,sal)
windows()
cor(sal,exp)
## [1] 0.9782416
m1<-lm("sal~exp",data=salary)
m1
##
## Call:
## lm(formula = "sal~exp", data = salary)
##
## Coefficients:
## (Intercept) exp
## 25792 9450
summary(m1)
##
## Call:
## lm(formula = "sal~exp", data = salary)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7958.0 -4088.5 -459.9 3372.6 11448.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25792.2 2273.1 11.35 5.51e-12 ***
## exp 9450.0 378.8 24.95 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5788 on 28 degrees of freedom
## Multiple R-squared: 0.957, Adjusted R-squared: 0.9554
## F-statistic: 622.5 on 1 and 28 DF, p-value: < 2.2e-16
pv<-predict(m1,salary)
pv
## 1 2 3 4 5 6 7
## 36187.16 38077.15 39967.14 44692.12 46582.12 53197.09 54142.09
## 8 9 10 11 12 13 14
## 56032.08 56032.08 60757.06 62647.05 63592.05 63592.05 64537.05
## 15 16 17 18 19 20 21
## 68317.03 72097.02 73987.01 75877.00 81546.98 82491.97 90051.94
## 22 23 24 25 26 27 28
## 92886.93 100446.90 103281.89 108006.87 110841.86 115566.84 116511.84
## 29 30
## 123126.81 125016.80
pv1<-as.data.frame(pv)
pv1
## pv
## 1 36187.16
## 2 38077.15
## 3 39967.14
## 4 44692.12
## 5 46582.12
## 6 53197.09
## 7 54142.09
## 8 56032.08
## 9 56032.08
## 10 60757.06
## 11 62647.05
## 12 63592.05
## 13 63592.05
## 14 64537.05
## 15 68317.03
## 16 72097.02
## 17 73987.01
## 18 75877.00
## 19 81546.98
## 20 82491.97
## 21 90051.94
## 22 92886.93
## 23 100446.90
## 24 103281.89
## 25 108006.87
## 26 110841.86
## 27 115566.84
## 28 116511.84
## 29 123126.81
## 30 125016.80
final<-cbind(salary,pv1)
final
## exp sal pv
## 1 1.1 39343 36187.16
## 2 1.3 46205 38077.15
## 3 1.5 37731 39967.14
## 4 2.0 43525 44692.12
## 5 2.2 39891 46582.12
## 6 2.9 56642 53197.09
## 7 3.0 60150 54142.09
## 8 3.2 54445 56032.08
## 9 3.2 64445 56032.08
## 10 3.7 57189 60757.06
## 11 3.9 63218 62647.05
## 12 4.0 55794 63592.05
## 13 4.0 56957 63592.05
## 14 4.1 57081 64537.05
## 15 4.5 61111 68317.03
## 16 4.9 67938 72097.02
## 17 5.1 66029 73987.01
## 18 5.3 83088 75877.00
## 19 5.9 81363 81546.98
## 20 6.0 93940 82491.97
## 21 6.8 91738 90051.94
## 22 7.1 98273 92886.93
## 23 7.9 101302 100446.90
## 24 8.2 113812 103281.89
## 25 8.7 109431 108006.87
## 26 9.0 105582 110841.86
## 27 9.5 116969 115566.84
## 28 9.6 112635 116511.84
## 29 10.3 122391 123126.81
## 30 10.5 121872 125016.80