x=scan()
0  -1  -23   1.4
0  -1  -15   0.3
0  -1  -15   1.4
0  -1  -14   1.6
0  -1  -13   1.65
0  -1  -12   1.49
0  -1  -10.5 1.25
0  -1  -8    1.7
0  -1  -6    1.1
0  -1  -3    1.5
0  -1  -1    2.1
0  -1  -1    2.3
0  -1  -0.5  1.6
0  -1   1    1.1
0  -1   1    1.7
0  -1   1.5  2.2
0  -1   2    2.3
0  -1   4    3.2
0  -1   5    2
0  -1   5.5  2.35
0  -1   9.5  2.3
0  -1  10    2.1
0  -1  11    2.6
0  -1  12    2.4
0  -1  13    2.5
0  -1  15    2.9
0  -1  15    2.3
0  -1  19    2.7
0  -1  20    2.5
0  -1  20.5  2.9
0  -1  22    2.8
0  -1  23    2.9
0  -1  23    3
0  -1  23    3.1
0  -1  33    4.2
0  -1  36    3.4
0  -1  37    3.3
0  -1  40    2.65
0  -1  41    4.2
0  -1  41.5  4.3
0  -1  48    3.3
1  0 -17.5  1.4
1  0  -5    0.55
1  0  -5    0.9
1  0  -4    0.6
1  0   1    1
1  0   2    1
1  0   3    1.3
1  0   3    1.1
1  0   5    1.05
1  0   5    0.35
1  0   8    0.7
1  0   9    1
1  0   9    1.25
1  0  10    2
1  0  11    1.8
1  0  11.5  1.5
1  0  12    1.7
1  0  18    1.2
1  0  20    1.3
1  0  22    1.3
1  0  22    1.8
1  0  22.5  1.2
1  0  26    1.2
1  0  30    1.3
1  0  32    1.4
1  0  32    2.5
1  0  39    1.8
1  0  41    1.3
1  0  42    2.2
1  0  42    1.5
1  0  44    1.4
1  0  53    1.7
1  0  56    1.8
1  1 -16    0.4
1  1  -12   0.6
1  1  -9    0.7
1  1  -8.5  0.4
1  1  -8.5  0.55
1  1  -2    0.7
1  1  -1    0.8
1  1   6    1
1  1  19    0.8
1  1  21    0.7
1  1  23    0.75
1  1  25    0.9
1  1  28    0.8
1  1  32    0.55
1  1  38    0.8
1  1  40    1

##

fluoride     = x[seq(1,360,4)]
natural      = x[seq(2,360,4)]
deprivation  = x[seq(3,360,4)]
decay.score  = x[seq(4,360,4)]
type=fluoride+(natural==1)
ds=data.frame(fluoride,natural,deprivation,decay.score,type) 
head(ds)

plot(ds$deprivation,ds$decay.score, ylim=c(0,5),col=type+1,pch=19)

fit0=lm(decay.score~deprivation,data=subset(ds,type==0))
points( ds$deprivation[ds$type==0], fit0$fitted.values, col=1, type="l" )

fit1=lm(decay.score~deprivation,data=subset(ds,type==1))
points( ds$deprivation[ds$type==1], fit1$fitted.values, col=2,type="l" ) 

fit2=lm(decay.score~deprivation,data=subset(ds,type==2))
points( ds$deprivation[ds$type==2], fit2$fitted.values, col=3,type="l" ) 


fit.all=lm(decay.score~deprivation+,data=subset(ds,type==2))



mult.fit=glm(ds$decay.score~ds$deprivation+as.factor(ds$type),family=poisson)

summary(mult.fit)

plot(ds$deprivation,mult.fit$fitted.values,col=ds$type+1,pch=19,cex=0.5)
points(ds$deprivation,ds$decay.score,col=ds$type+1)


add.fit=glm(ds$decay.score~ds$deprivation+as.factor(ds$type)+
                          ds$deprivation*as.factor(ds$type) , family=gaussian)
summary(add.fit)

ds$dep = ds$deprivation + 35
add.fit=glm(ds$decay.score~ds$dep+as.factor(ds$type)+
                          ds$dep*as.factor(ds$type) , family=gaussian)
summary(add.fit)

add.fit=glm(ds$decay.score~ds$dep+
                          ds$dep*as.factor(ds$type) , family=gaussian)
summary(add.fit)