Daten Tschernobyl

ort<-c(„Pripjet“, „Chistogalovka“, „Lelev“, „Tschernobyl“, „Rudki“, „Orevichi“, „Kiew“, „Tschernikow“, „Tscherkassy“, „Minsk“, „Donezk“, „Wien“, „Oesterreich“, „Stockholm“, „Gaevle“, „SuedBayern“, „Konstanz“, „Irland“, „Stuttgart“, „Chilton“, „Schottland“, „Japan“, „Japan2“)
regen<-c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1)
dist<-c(3.16, 5.62, 7.94, 14.13, 15.85, 28.18, 89.13, 125.89, 281.84, 316.23, 707.95, 1000.00, 1000.00, 1122.02, 1258.93, 1258.93, 1584.89, 1584.89, 1590.54, 1995.26, 1995.26, 12589.25, 12589.25)
bq<-c(5300, 2000, 2100, 2000, 800, 2000, 21, 55, 12, 20, 6, 3, 53, 1.5, 31, 81, 31, 16, 1.5, 1.5, 17, 0.15, 0.85)
daten<-data.frame(ort, regen, dist, bq)
> daten
             ort regen     dist      bq
1        Pripjet     0     3.16 5300.00
2  Chistogalovka     0     5.62 2000.00
3          Lelev     0     7.94 2100.00
4    Tschernobyl     0    14.13 2000.00
5          Rudki     0    15.85  800.00
6       Orevichi     0    28.18 2000.00
7           Kiew     0    89.13   21.00
8    Tschernikow     0   125.89   55.00
9    Tscherkassy     0   281.84   12.00
10         Minsk     0   316.23   20.00
11        Donezk     0   707.95    6.00
12          Wien     0  1000.00    3.00
13   Oesterreich     1  1000.00   53.00
14     Stockholm     0  1122.02    1.50
15        Gaevle     1  1258.93   31.00
16    SuedBayern     1  1258.93   81.00
17      Konstanz     1  1584.89   31.00
18        Irland     1  1584.89   16.00
19     Stuttgart     0  1590.54    1.50
20       Chilton     0  1995.26    1.50
21    Schottland     1  1995.26   17.00
22         Japan     0 12589.25    0.15
23        Japan2     1 12589.25    0.85
> daten2<-data.frame(log(bq), log(dist), regen)
> par(mex=0.5)
> pairs(daten2, gap=0, cex.labels=0.9)
> alles<-lm(log(bq)~log(dist)+ regen)
> alles

Call:
lm(formula = log(bq) ~ log(dist) + regen)

Coefficients:
(Intercept)    log(dist)        regen  
     10.522       -1.360        2.723  

> aov(alles)
Call:
   aov(formula = alles)

Terms:
                log(dist)     regen Residuals
Sum of Squares  152.87486  26.78701   7.60440
Deg. of Freedom         1         1        20

Residual standard error: 0.6166197 
Estimated effects may be unbalanced
> summary(alles)

Call:
lm(formula = log(bq) ~ log(dist) + regen)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.37021 -0.37287 -0.05441  0.21542  1.61984 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 10.52246    0.34904  30.147  < 2e-16 ***
log(dist)   -1.36027    0.06316 -21.536 2.62e-15 ***
regen        2.72254    0.32436   8.394 5.51e-08 ***
---
Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 

Residual standard error: 0.6166 on 20 degrees of freedom
Multiple R-Squared: 0.9594,     Adjusted R-squared: 0.9553 
F-statistic: 236.3 on 2 and 20 DF,  p-value: 1.219e-14 

> regenweg<-lm(log(bq)~log(dist))
> regenweg

Call:
lm(formula = log(bq) ~ log(dist))

Coefficients:
(Intercept)    log(dist)  
      9.803       -1.091  

> distweg<-lm(log(bq)~ regen)
> distweg

Call:
lm(formula = log(bq) ~ regen)

Coefficients:
(Intercept)        regen  
     3.7784      -0.8247  

> aov(distweg)
Call:
   aov(formula = distweg)

Terms:
                    regen Residuals
Sum of Squares    3.31193 183.95434
Deg. of Freedom         1        21

Residual standard error: 2.959684 
Estimated effects may be unbalanced
> aov(regenweg)
Call:
   aov(formula = regenweg)

Terms:
                log(dist) Residuals
Sum of Squares  152.87486  34.39141
Deg. of Freedom         1        21

Residual standard error: 1.279721 
Estimated effects may be unbalanced