podatki <- data.frame("ID"= c(1, 2, 3, 4, 5),
                      "Visina" = c(170, 175, 177, 180, 182),
                      "Starost" = c(20, 22, 22, 23, 24),
                      "Spol" = c("Z", "Z", "M", "M", "M"))
mean(podatki$Visina)
## [1] 176.8
summary(podatki[ , c (-1,-4)])
##      Visina         Starost    
##  Min.   :170.0   Min.   :20.0  
##  1st Qu.:175.0   1st Qu.:22.0  
##  Median :177.0   Median :22.0  
##  Mean   :176.8   Mean   :22.2  
##  3rd Qu.:180.0   3rd Qu.:23.0  
##  Max.   :182.0   Max.   :24.0
podatki$Teza <- c(65,60,70,72,80)

Izdracunaj BDM indeks

podatki$BMI <- round(podatki$Teza / (podatki$Visina/100)^2, 2)
#install.packages("pastecs")
library(pastecs)

round(stat.desc(podatki[ , c(-1,-4)]), 3)
##               Visina Starost    Teza     BMI
## nbr.val        5.000   5.000   5.000   5.000
## nbr.null       0.000   0.000   0.000   0.000
## nbr.na         0.000   0.000   0.000   0.000
## min          170.000  20.000  60.000  19.590
## max          182.000  24.000  80.000  24.150
## range         12.000   4.000  20.000   4.560
## sum          884.000 111.000 347.000 110.790
## median       177.000  22.000  70.000  22.340
## mean         176.800  22.200  69.400  22.158
## SE.mean        2.083   0.663   3.370   0.732
## CI.mean.0.95   5.784   1.842   9.358   2.032
## var           21.700   2.200  56.800   2.677
## std.dev        4.658   1.483   7.537   1.636
## coef.var       0.026   0.067   0.109   0.074
#install.packages("psych")
library(psych)
describe(podatki$Visina)
##    vars n  mean   sd median trimmed  mad min max range  skew kurtosis   se
## X1    1 5 176.8 4.66    177   176.8 4.45 170 182    12 -0.29    -1.73 2.08