Discrete Random Variables

In chapter 5 we began the study of probability, needed for inference in the process of statistics. In chapter 6 we will integrate the idea of a probability distribution with the topics we covered in chapters 2 and 3. In particular, we will apply the way we describe data and distributions to probability distributions. When we describe a probability distribution, we will include:

•Center- based on the probability, what is the most typical or expected outcome?

•Variation- a way to describe how much the outcomes in the probability distribution vary.

•Shape of the distribution- is the probability distribution skewed or bell-shaped?

The process of statistics

By the end of this chapter, you will be able to:

•identify and interpret discrete and continuous random variables.

•identify a discrete probability distribution.

•construct a probability histogram.

•find the mean and standard deviation of a discrete random variable.

•interpret the mean as expected value.

•describe Bernoulli Trials for a binomial variable.

•recognize and describe a binomial random variable.

•compute probability for a discrete binomial random variable.

•find the mean and standard deviation of a discrete random variable.

•determine when a discrete random variable is bell-shaped.

•determine typical and unusual results in a binomial experiment.