Applications of Statistical Techniques Module
Welcome to the Applications of Statistical Techniques Module home page! Please review all of the information and links provided below.
This module will introduce you to a set of advanced business analytic techniques. We define advanced business analytics as a set of tools and techniques to assist in key business decisions. While the emphasis is on understanding and implementing results, you should always keep in mind the broader context of decision making, including the analysis of risk described in the Ask an Actuary reading.
After completing this module, you will be able to:
- Use the computing environment R to analyze data
- Explain the differences between ordinary least squares and generalized linear models (GLM)
- Use software to estimate the parameters of a GLM
- Select an appropriate GLM for a given data set, including variable selection and diagnostics
- Use GLMs to perform classification ratemaking
- Explain how the generalized linear mixed model incorporates credibility
- Use various models, including GLMs, to estimate variability in reserve estimates
- Understand how Bayesian and bootstrap methods are used in estimating variability in reserve estimates
- Understand how clustering methods can be used in setting predictor variables