#variables
x<-c(10,100,1000,10000,100000);x
## [1] 1e+01 1e+02 1e+03 1e+04 1e+05
y<-c(22,300,3800,40000,600000);y
## [1] 22 300 3800 40000 600000
z<-c(10,250,9000,12000,200000);z
## [1] 10 250 9000 12000 200000
#creating data frame
data.frame(x,y,z)
## x y z
## 1 1e+01 22 10
## 2 1e+02 300 250
## 3 1e+03 3800 9000
## 4 1e+04 40000 12000
## 5 1e+05 600000 200000
#correlation
cor(x,y)
## [1] 0.9994765
cor(x,z)
## [1] 0.9981505
cor(y,z)
## [1] 0.9991731
#regression
lm(y~x+z)
##
## Call:
## lm(formula = y ~ x + z)
##
## Coefficients:
## (Intercept) x z
## -5324.470 3.518 1.265
#Analysis of variance
library(linearModel)
aov(lm(y~x+z))
## Call:
## aov(formula = lm(y ~ x + z))
##
## Terms:
## x z Residuals
## Sum of Squares 278344244624 179991933 111663750
## Deg. of Freedom 1 1 2
##
## Residual standard error: 7472.073
## Estimated effects may be unbalanced
summary(lm(y~x+z))
##
## Call:
## lm(formula = y ~ x + z)
##
## Residuals:
## 1 2 3 4 5
## 5298.6 4956.4 -5778.6 -5032.0 555.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5324.4702 3844.4490 -1.385 0.300
## x 3.5176 1.4070 2.500 0.130
## z 1.2651 0.7046 1.796 0.214
##
## Residual standard error: 7472 on 2 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9992
## F-statistic: 2494 on 2 and 2 DF, p-value: 0.0004008
#visualization
hist(x)
hist(y)
hist(z)
plot(lm(y~x))
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
