Reference no: EM132588769

1. Types of Variables - As the subject heading says, there are two different kinds of variables - discrete and continuous. What are the differences between these two types? How might measuring them differ? And why is using a random collection (sampling) method so important?

2. Discrete Probabilty Distributions - Now we get to apply the discussion of probability to more practical uses. Here we look at discrete probability. While there are several different kinds of discrete probability functions (or PDF's), three in particular are most commonly used. These are the binomial, Poisson and hypergeometric. What are the characteristics of each? Where and how are they used? Have you ever seen or even used any of these?

3. Continuous Probability Functions - As is the case with discrete PDF's, there are a number of continuous PDF's such as the exponential and uniform distributions. But the most important by far is the normal distribution (or bell curve). What are the characteristics of the normal distribution that make it the foundation of the more important inferential statistical tools we will be learning about in the coming weeks? Why do these characteristics allow us to trust the results of using these tools?

Business Statistics, Ch. 5: Discrete Distributions

4. Define a random variable in order to differentiate between a discrete distribution and a continuous distribution.

5. Determine the mean, variance, and standard deviation of a discrete distribution.

6. Define and how is it applied - Binomial Distribution

Business Statistics, Ch. 6: Continuous Distributions

7. Solving for Probabilities Using the Normal Curve?

8. Example - Using the Normal Curve to Approximate Binomial Distribution Problems.