Priscila-Tandalla.R

datos<-datasets::CO2


datos<-datasets::iris
especies<-datos$Species
TDF_especies<-data.frame(table(especies))
ni<-TDF_especies$Freq

# Ajustar modelo lineal
modelo <- lm(Petal.Length ~ Petal.Width, data=iris)

# Predicción para Petal.Width = 0.75
predict(modelo, newdata = data.frame(Petal.Width = 0.75))

##        1 
## 2.756013

######
datos<-datasets::CO2
concentracion<-datos$conc
hist(concentracion)

##########rios####

rivers

##   [1]  735  320  325  392  524  450 1459  135  465  600  330  336  280  315  870
##  [16]  906  202  329  290 1000  600  505 1450  840 1243  890  350  407  286  280
##  [31]  525  720  390  250  327  230  265  850  210  630  260  230  360  730  600
##  [46]  306  390  420  291  710  340  217  281  352  259  250  470  680  570  350
##  [61]  300  560  900  625  332 2348 1171 3710 2315 2533  780  280  410  460  260
##  [76]  255  431  350  760  618  338  981 1306  500  696  605  250  411 1054  735
##  [91]  233  435  490  310  460  383  375 1270  545  445 1885  380  300  380  377
## [106]  425  276  210  800  420  350  360  538 1100 1205  314  237  610  360  540
## [121] 1038  424  310  300  444  301  268  620  215  652  900  525  246  360  529
## [136]  500  720  270  430  671 1770

sum(rivers < 3500)

## [1] 140

datos<-datasets::ChickWeight
dietas<-datos$Diet









# Cargar datos
data(ChickWeight)

# Estimar parámetros lognormales
log_weights <- log(ChickWeight$weight)
mu <- mean(log_weights)
sigma <- sd(log_weights)

prob <- plnorm(100, meanlog = mu, sdlog = sigma) - plnorm(50, meanlog = mu, sdlog = sigma)
n <- nrow(ChickWeight)
expected_count <- n * prob
expected_count

## [1] 214.7679

datos<-rivers
hist(datos)