The purpose of this paper is to discuss certain limitations and rationales when it comes to the distribution applications in regards to the approximation of the different types of probability distributions and how it is that we would perceive to apply the different distributions (i.e. discrete and continuous) probabilities. This is in relation to our marketing department and the matters that they face in the 800 phone bank section.

I will look at certain quantitative objects that actually need to be checked regarding this. We would be looking at how to implement new procedures for gathering additional relevant and helpful information from our clients when they call the service center.

When looking at quantitative data there are two kinds that are in effect. They are discrete and continuous data as I mentioned earlier. In fact, numerical data will only fall in one of these two categories. Values of discrete nature are ones that have values which can be added up or counted by means of integers. Discrete data can only actually be taken in full values unlike that of continuous data. Continuous variables in fact are ones that come about as a result of any value that is or can be actually between any two values that are given. An example of a discrete probability would be something such as the number of sunny days in a given week or the number of correct answers on a college quiz (Western Server Media, 2008). Continuous data can be derived by asking the question of it is feasible for data to take on values that are a fraction or a decimal point per se. If the answer to this question can be answered with a yes, then it is most likely continuous data that you have. An example of continuous data would be something like the length of time it would take a light bulb to burn out could be labeled as continuous data (i.e. would it take 700hrs or 700.5 or maybe even 700.6589.) The actual answer would be yes it could actually take any of the three times to do so (Stroud, J. D., 2008).

Now to switch gears and address the situation with the 800 phone bank numbers I would look at asking questions from a discrete standpoint such as were the items fresh or of a stale nature. It would be discrete because you could answer it by saying a simple yes or no to the question asked. Another question that could be asked is if the snack item was damaged? And it two could be answered simply with a yes or no which would equate to it being of discrete nature as well. Now if I wanted to look at making it a continuous probability, I would go with something such as how many callers were advised or instructed that our snack products would not be in stock any longer for them to purchase. This will be continuous because these numbers here will change on a regular basis (WSM, 2008).

I would say that we could learn a lot from our callers by examining their demographic and what they say in regards to us telephonically. It is very important though to make sure that we get and retain all information as it becomes available to us.

**References:**

Stroud, J. Delayne (2008). *Challenges of Discrete and Attribute Data Measurement.* Retrieved on January 11, 2011 from http://finance.isixsigma.com/library/content/c06062

Western Server Media, (2008). Continuous and Discrete Data. Retrieved on January 11, 2012 from http://westernreservepublicmedia.org/**Cached**