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# One-way ANOVA
data(PlantGrowth)   # weight of plants under 3 conditions

aov_model <- aov(weight ~ group, data = PlantGrowth)
summary(aov_model)

# Post-hoc: Tukey HSD
TukeyHSD(aov_model)

# Two-way ANOVA
data(ToothGrowth)
aov2 <- aov(len ~ supp * dose, data = ToothGrowth)
summary(aov2)

# Logistic regression
# Create a binary outcome
data(mtcars)
mtcars$am <- factor(mtcars$am, labels = c("automatic", "manual"))

log_model <- glm(am ~ mpg + wt + hp,
                 data   = mtcars,
                 family = binomial)
summary(log_model)

# Coefficients as odds ratios
exp(coef(log_model))
exp(confint(log_model))

# Fitted probabilities
fitted_probs <- predict(log_model, type = "response")
predicted_class <- ifelse(fitted_probs > 0.5, "manual", "automatic")

# Confusion matrix (manual)
table(Predicted = predicted_class, Actual = mtcars$am)

# Accuracy
mean(predicted_class == as.character(mtcars$am))

# Likelihood ratio test (compare to null model)
null_model <- glm(am ~ 1, data = mtcars, family = binomial)
anova(null_model, log_model, test = "LRT")

# Pseudo R-squared (McFadden)
1 - log_model$deviance / null_model$deviance