Probability And Mathematical Statistics Theory Applications And Practice In R |work| Jun 2026

Theory provides criteria for selecting models. AIC (Akaike Information Criterion) focuses on predictive accuracy: $AIC = -2 \log L + 2k$ (where $k$ is number of parameters). BIC (Bayesian Information Criterion) adds a penalty for sample size: $BIC = -2 \log L + k \log n$.

This is the process of making decisions about a population based on sample evidence. R is the industry standard for this practice, offering dedicated functions for: Comparing means between groups. ANOVA: Assessing variance across multiple categories. Theory provides criteria for selecting models

step_model <- step(model1, scope = list(lower = ~1, upper = ~ wt + hp + qsec + am), direction = "both", trace = 0) summary(step_model) scope = list(lower = ~1

Compute a bootstrap confidence interval for the median. direction = "both"