Applied Managerial Decision-Making: Data Aggregation

by on December 20th, 2010
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The purpose of this paper is to shed some light in regards to Widgecorp, Inc. in relation to figuring out what methods could be used to reduce the data aggregation so that it shows what underlying patterns and parameters that this data consists of. We will be using data from given reports that contain an overview, trends, employment, and population detail of the area that is located in the zip code area of 60614. I will actually be listing a variable for each one of the areas of the perspective report. In addition to this, I will also try to give a synopsis of why we should collapse the categories and how it should be done to accomplish this task.

After looking at the excel spreadsheet from the general summary report of 2000. There was a deviation above the mean income, which with this, the company could look at starting to devise the marketing that they do and its activities and gear them towards those persons who are in the middle than when their income rises. With that being said, the brand is already at tops per se.

Another part of this is if we look at race, we can see that depending on certain lifestyles and preferences, the regional sales manager can actually make and put together different sales strategies for the persons belonging to the perspective categories.

When we look at the data of the occupation and summary report for this perspective year. We would minimize this by breaking it down into smaller categories such as: Private transportation, public transportation, and or those with no transportation. Being that there are different types of public transportation that are used, we could add those numbers up and list them in this area to get an overall percentage of almost 40 percent. We will then look at those that use private transportation since more people use this because of the numbers being over 50 percent by means of personal vehicle, bicycles or car pools.

The census trend is another area where data can be minimized looking tat the years from 1980 to 2000. You can do this by deciphering the changes over the years in regards to the different incomes made by low income females or males to those of the same gender of high earners. These variables will be looked at to see if the income ranges changed significantly over a certain number of years by the data that was given for those individuals and how they did from a number standpoint in relation to their buying of snack foods. This can also be done by separating the wages in increments from $1 to $24,999 and $25,000 to $49,999 and so on. It will make numbers and categories easier to follow than that of the typical format that is normally used (Marketing Power, Inc., 2012).

The fourth variable is the income summary report. We can focus on those earners that make anywhere from $1 to $24,999. And those that make $25,000 to 49,999. The final one would be for those making 50,000 plus. This will be another area that the regional sales manager can go over and add his overall numbers after he has broken down and analyzed the data of each area looked at.


Bowerman, O. M. (2012). Essentials of Business Statistics. McGraw- Hill Irwin.

Marketing Power, Inc., (2012). Marketing Power. Retrieved on January 17, 2012 from

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