diff --git a/Non-LinearModels/CompaniesData/Script_Companies.R b/Non-LinearModels/CompaniesData/Script_Companies.R
index 659f52a..f8a3433 100644
--- a/Non-LinearModels/CompaniesData/Script_Companies.R
+++ b/Non-LinearModels/CompaniesData/Script_Companies.R
@@ -1,4 +1,9 @@
 #MULTIPLE REGRESSION ANALYSIS
+#algorithm
+#Linear model
+#stepwise
+#shapiro (residuals)
+#box-cox
 
 #Isabela Pereira Lima Dias
 
@@ -101,3 +106,130 @@ confint(step_companies,level=0.95)
 plot_summs(step_companies,colors = "#440184")
 # standardize variables liquidy and assets
 plot_summs(step_companies,scale=TRUE,colors = "#440184")
+
+plot_summs(step_companies, scale = TRUE, plot.distributions = TRUE,
+           inner_ci_level = .95, colors = "#440184")
+
+plot_summs(companies_model, step_companies, scale = TRUE, plot.distributions = TRUE,
+           inner_ci_level = .95, colors = c("lightblue3", "#440154FF"))
+#SHAPIRO-FRANCIA test
+sf.test(step_companies$residuals)
+
+companies %>%
+  mutate(residuals = step_companies$residuals) %>%
+  ggplot(aes(x = residuals)) +
+  geom_histogram(color = "white", 
+                 fill = "#440154FF", 
+                 bins = 30,
+                 alpha = 0.6) +
+  labs(x = "Residuals",
+       y = "Frequency") + 
+  theme_bw()
+
+companies %>%
+  mutate(residuals = step_companies$residuals) %>%
+  ggplot(aes(x = residuals)) +
+  geom_histogram(aes(y = ..density..), 
+                 color = "white", 
+                 fill = "#440154FF", 
+                 bins = 30,
+                 alpha = 0.6) +
+  stat_function(fun = dnorm, 
+                args = list(mean = mean(step_companies$residuals),
+                            sd = sd(step_companies$residuals)),
+                size = 2, color = "lightblue3") +
+  scale_color_manual(values = "lightblue1") +
+  labs(x = "Residuals",
+       y = "Frequency") +
+  theme_bw()
+#box-cox tranformation
+lambda_bc <- powerTransform(companies$return)
+lambda_bc
+companies$returnbc <- (((companies$return ^lambda_bc$lambda)-1)/lambda_bc$lambda)
+
+bc_model <- lm(formula= returnbc ~. -company -return ,data=companies)
+summary(bc_model)
+
+step_bc_model <- step(bc_model, k=k)
+summary(step_bc_model)
+sf.test(step_bc_model$residuals)
+
+companies %>%
+  mutate(residuals = step_bc_model$residuals) %>%
+  ggplot(aes(x = residuals)) +
+  geom_histogram(aes(y = ..density..),
+                 color = "white",
+                 fill = "#287D8EFF",
+                 bins = 30,
+                 alpha = 0.6) +
+  stat_function(fun = dnorm, 
+                args = list(mean = mean(step_bc_model$residuals),
+                            sd = sd(step_bc_model$residuals)),
+                size = 2, color = "deeppink4") +
+  scale_color_manual(values = "deeppink3") +
+  labs(x = "Residuals",
+       y = "Frequency") +
+  theme_bw()
+
+
+#Summary of the models
+
+
+export_summs(step_companies, step_bc_model,
+             model.names = c("Linear Model ","Box-Cox Model "),
+             scale = F, digits = 6)
+
+confint(step_bc_model, level = 0.95) #  5%
+
+#Plots 
+plot_summs(step_bc_model, colors = "deeppink4") 
+
+plot_summs(step_bc_model, scale = TRUE, colors = "deeppink4")
+
+plot_summs(step_bc_model, scale = TRUE, plot.distributions = TRUE,
+           inner_ci_level = .95, colors = "deeppink4")
+
+
+plot_summs(step_companies, step_bc_model, scale = T, plot.distributions = TRUE,
+           inner_ci_level = .95, colors = c("deeppink4", "lightblue2"))
+
+predict(object = step_bc_model, 
+        data.frame(disclosure = 50,
+                   liquidity = 14,
+                   assets = 4000),
+        interval = "confidence", level=0.95)
+(((3.702015 * -0.02256414) + 1)) ^ (1 / -0.02256414)
+
+
+companies$yhat_step_companies <- step_companies$fitted.values
+companies$yhat_step_bc_model <- (((step_bc_model$fitted.values*(lambda_bc$lambda))+
+                                    1))^(1/(lambda_bc$lambda))
+companies %>%
+  select(company, return, yhat_step_companies, yhat_step_bc_model) %>%
+  kable() %>%
+  kable_styling(bootstrap_options = "striped", 
+                full_width = F, 
+                font_size = 14)
+
+
+companies %>%
+  ggplot() +
+  geom_smooth(aes(x = return, y = yhat_step_companies, color = "Stepwise"),
+              method = "lm", se = F, formula = y ~ splines::bs(x, df = 5), size = 1.5) +
+  geom_point(aes(x = return, y = yhat_step_companies),
+             color = "deeppink3", alpha = 0.6, size = 2) +
+  geom_smooth(aes(x = return, y = yhat_step_bc_model, color = "Stepwise Box-Cox"),
+              method = "lm", se = F, formula = y ~ splines::bs(x, df = 5), size = 1.5) +
+  geom_point(aes(x = return, y = yhat_step_bc_model),
+             color = "lightblue2", alpha = 0.6, size = 2) +
+  geom_smooth(aes(x = return, y = return), method = "lm", formula = y ~ x,
+              color = "grey30", size = 1.05,
+              linetype = "longdash") +
+  scale_color_manual("Models:", 
+                     values = c("lightblue2", "deeppink3")) +
+  labs(x = "Return", y = "Fitted Values") +
+  theme(panel.background = element_rect("white"),
+        panel.grid = element_line("grey95"),
+        panel.border = element_rect(NA),
+        legend.position = "bottom")
+