Advanced-Reliability-Concepts

Materials & studying resources for Advanced Topics in Reliability course from AFIT that I learned myself

Course Learning Objectives

After completing this course, students will:

1. Thoroughly understand the basic concepts on which statistical methods for life-data analyses are based, specifically:

   • How life-data analysis methods differ from other statistical methods
   • The importance of statistical censoring
   • Differences between right, left, and interval censoring
   • Why all data is interval censored data
   • How to use nonparametric methods for fitting models to data

2. Be able to implement maximum likelihood (ML) estimation, specifically:

   • How to construct likelihood functions for various types of data
   • How to fit data to a parametric model by maximizing likelihood functions analytically, graphically, and computationally
   • The importance of ML estimation in the frequentist and Bayesian domains
   • The purpose and importance of the relative likelihood and profile likelihood functions

3. Understand various point and interval estimation techniques, specifically:

   • How compute estimates for parameters and functions of parameters from data
   • How to compute confidence intervals and credible intervals for parameters and functions of parameters from data
   • How to apply the Delta method to compute uncertainty propagation for an arbitrary function of the parameters
   • Advantages and disadvantages between various procedures used to compute confidence intervals

4. Use software to analyze real-world reliability data and communicate the results of their analyses, specifically:

   • Students will use R & RStudio to compute results from real-world life data
   • Students will communicate analysis techinques and results together using rmarkdown
   • Students will build interactive plots and tables using shiny