#Multi Regresión Lineal con APROBADO/REPROBADO
nf=as.matrix(acta1[6])
ex=as.matrix(acta1[5])
c3=as.matrix(acta1[4])
c2=as.matrix(acta1[3])
c1=as.matrix(acta1[2])

c1<-c1[2:101] #eliminamos la primera fila
c2<-c2[2:101]
c3<-c3[2:101]
ex<-ex[2:101]
nf<-nf[2:101]

ex<-as.numeric(ex)
nf<-as.numeric(nf)
c3<-as.numeric(c3)
c2<-as.numeric(c2)
c1<-as.numeric(c1)

cf=1:100

for(i in 1:100){
  if(is.na(nf[i])){cf[i]=0} 
  else if(nf[i]>39){cf[i]=1} 
  else{cf[i]=0}
}

fit <- lm(cf ~ c1+c2+c3+ex,na.action=na.omit)


#parte ANOVA
c1=as.matrix(acta1[2])
c2=as.matrix(acta1[3])
c3=as.matrix(acta1[4])

c1<-c1[2:101] #eliminamos la primera fila
c2<-c2[2:101]
c3<-c3[2:101]

c1<-as.numeric(c1) # pasamos losdatos a numeric
c2<-as.numeric(c2)
c3<-as.numeric(c3)

nc=c(c1,c2,c3) #concatena vectores

cat1<-seq(from = 1, to = 1, length.out = 100)#crea una secuencia de largo 100
cat2<-seq(from = 2, to = 2, length.out = 100)
cat3<-seq(from = 3, to = 3, length.out = 100)

cat=c(cat1,cat2,cat3)

RegLin<-lm(nc ~ cat) #regresión lineal
anova(RegLin)