# Lesson 1: Overview Printer-friendly version

This lesson is an overview of the course content as well as a review of some advanced statistical concepts involving discrete random variables and distributions, relevant for STAT 504 -- Analysis of Discrete Data. This Lesson assumes that you have glanced through the review materials included in the Start Here! block.

#### Key concepts:

• Discrete data types
• Discrete distributions: Binomial, Poission, Multinomial
• Likelihood & Loglikelhood
• Observed & Expected Information
• Likelihood based Confidence Intervals & Tests: Wald Test, Likelihood-ratio test, Score test

#### Objectives:

• Learn what discrete data are and their taxonomy
• Learn the properties of Binomial, Poission and Multinomial distributions
• Understand the basic principles of likelihood-based inference and how to apply it to tests and intervals regarding population proportions
• Introduce the basic SAS and R code

#### Useful links:

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The outline below can be viewed as a general template of how to approach data analysis regardless of the type of statistical problems you are dealing with. For example, you can model a continuous response variable such as income, or a discrete response such as a true proportion of U.S. individuals who support new health reform. This approach has five main steps. Each step typically requires an understanding of a number of elementary statistical concepts, e.g., a difference between a parameter to be estimated and the corresponding statistic (estimator).

#### The General Data Analysis Approach 