This is the third assignment for the class

Trabalho final por Anabella Sgarbi Pereira

HW3 for DAT 301

Midterm Exam Objectives Understand the definition and relationship between PDFs and CDFs, including their non-parametric estimators: the empirical distribution function and kernel density estimation (KDE). Estimate sampling distributions using simulation-based methods, specifically the bootstrap. Derive point estimates of parameters using the method of moments and maximum likelihood estimation (MLE). Describe the asymptotic (normal) and bootstrap sampling distributions of maximum likelihood estimators. Apply all the above inferential procedures in a programming environment to perform numerical data analysis.