diff --git a/scripts/ce089-06.R b/scripts/ce089-06.R
index 03e668c6c532cf0406ff98ce11e688529ea90552..bbd05609dd913191cee4bad5556531773cf0524d 100644
--- a/scripts/ce089-06.R
+++ b/scripts/ce089-06.R
@@ -63,8 +63,8 @@ cbind(n0 = rowMeans(b0) - coef(n0),
       n1 = rowMeans(b1) - coef(n1))
 
 # Variância.
-cbind(n0 = apply(b0, MARGIN = 1, var),
-      n1 = apply(b1, MARGIN = 1, var))
+round(cbind(n0 = apply(b0, MARGIN = 1, var),
+            n1 = apply(b1, MARGIN = 1, var)), digits = 5)
 
 # Função para calcular o erro quadrático médio.
 eqm <- function(x) {
@@ -72,8 +72,8 @@ eqm <- function(x) {
 }
 
 # Erro quadrático médio.
-cbind(n0 = apply(b0, 1, FUN = eqm),
-      n1 = apply(b1, 1, FUN = eqm))
+round(cbind(n0 = apply(b0, 1, FUN = eqm),
+            n1 = apply(b1, 1, FUN = eqm)), digits = 5)
 
 #-----------------------------------------------------------------------
 # Curvas ajustadas de onde é possível determinar uma banda de confiança.
@@ -205,6 +205,7 @@ str(st)
 
 # Gráfico de pares.
 pairs(b0)
+pairs(b0$t)
 
 vcov(b0)
 cov2cor(vcov(b0))
@@ -381,3 +382,65 @@ rowSums(result)/N
 save.image(file = "my_results.RData")
 
 #-----------------------------------------------------------------------
+
+PaulaTb3.12 <-
+    structure(list(
+        vol = c(3.7, 3.5, 1.25, 0.75, 0.8, 0.7, 0.6, 1.1, 0.9, 0.9, 0.8,
+                0.55, 0.6, 1.4, 0.75, 2.3, 3.2, 0.85, 1.7, 1.8, 0.4,
+                0.95, 1.35, 1.5, 1.6, 0.6, 1.8, 0.95, 1.9, 1.6, 2.7,
+                2.35, 1.1, 1.1, 1.2, 0.8, 0.95, 0.75, 1.3),
+        razao = c(0.825, 1.09, 2.5, 1.5, 3.2, 3.5, 0.75, 1.7, 0.75,
+                  0.45, 0.57, 2.75, 3, 2.33, 3.75, 1.64, 1.6, 1.415,
+                  1.06, 1.8, 2, 1.36, 1.35, 1.36, 1.78, 1.5, 1.5, 1.9,
+                  0.95, 0.4, 0.75, 0.03, 1.83, 2.2, 2, 3.33, 1.9, 1.9,
+                  1.625),
+        resp = c(1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
+                 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1,
+                 0, 0, 1)),
+        .Names = c("vol", "razao", "resp"),
+        row.names = c(NA, 39L),
+        class = "data.frame")
+
+layout(1)
+
+#  Visualização dos dados.
+xyplot(resp ~ vol,
+       data = PaulaTb3.12,
+       groups = resp)
+
+#  Ajuste do modelo considerando resposta binária.
+m0 <- glm(resp ~ vol,
+          data = PaulaTb3.12,
+          family = binomial)
+summary(m0)
+
+# Estimativa da DL_50 a partir do modelo ajustado.
+dl <- -coef(m0)[1]/coef(m0)[2]
+
+# Curva ajustada.
+plot(resp ~ vol, data = PaulaTb3.12)
+curve(m0$family$linkinv(coef(m0)[1] + coef(m0)[2] * x),
+      add = TRUE)
+abline(v = dl, h = 0.5, lty = 2)
+
+dl_50 <- function(dataset, index) {
+    m0 <- glm(resp ~ vol,
+              data = dataset[index, ],
+              family = binomial)
+    dl <- -coef(m0)[1]/coef(m0)[2]
+    return(dl)
+}
+
+dl50 <- boot(data = PaulaTb3.12,
+             statistic = dl_50,
+             R = 2999)
+
+summary(dl50)
+
+hist(dl50)
+
+boot.ci(dl50,
+        type = "all",
+        index = c(1, 1))
+
+#-----------------------------------------------------------------------