Earn A Free Analysis: If you know of any other analytical tool that allows more than three dimensions (except OLAP tools), OR allows two levels of analysis simultaneously, OR allows substantial "what-if" simulations, OR allows high level (household or account) categorizations based upon collections of low level (persons or transactions) data, please email us. Examples of all of these capabilities follow.

NEW! - Free Financial Tabulations One of the most popular areas on our site is the financial data explanations, so we're adding some free financial tabulations. Bookmark this page - there's more to come. 10 different personal financial data items, along with people and household counts, sliced-and-diced by State and recoded earnings data Family income data, along with household counts, sliced-and-diced by State, vacancy and recoded family income data Household income data, along with household counts, sliced-and-diced by State, vacancy and recoded household income data A Simple 3-D Slicing and Dicing This serves as an introduction to the elements common to all of our analyses. It has counts of people and households based upon household income, number of automobiles, and citizenship. Example 1 Explanation and Output A Slightly More Complex Analysis A public health care official wants to study teenage mothers and their families. There are things being asked here that the census bureau does not explicitly record, such as household categorization based upon the number of teen mothers present, total number of mothers present, and others. Example 2 Explanation and Output A Simulation Example A heating oil retailer wants to analyze heating oil usage in a particular area.  But he wants to know about it in 2001, not 1989 (which is stored in PUMS 1990), so he wants to apply simulation factors to the data.  He will make adjustments to account for population changes, conversions from electric to oil heat, and changes in heating oil prices. Example 3 Explanation and Output Error Correction In this example, we use our tools to track down and correct a data error, while leaving the original file unchanged. Many Asian Indians are misclassified as having American Indian ancestry. We take steps to identify the errors, correct the errors, test our correction, and examine the results. Example 4 Explanation and Output 7 & 8 Dimensional Analyses Many Arab Americans feel that they are underrepresented in the census. According to our investigation, it looks like their concerns are well founded. We cross-reference Place of Birth, Primary Ancestry, Secondary Ancestry, Language and relationships within families in order to arrive at our conclusions. Example 5 Explanation and Output Basic Business Example The Acme Widget company wants to slice-and-dice sales figures based upon the revenue category of the customer, and size and color of widgets. It seems simple, but without our ability to create customer level dimensions based upon purchasing history, coupled with multidimensional capability, they would need to wait for their programmers to design, write, test, and debug custom code. Example 6 Output Identify High Revenue Customers The Acme Widget company wants to see if the 80/20 rule applies to them - does 80% of their revenue come from 20% of their customers? And whether it does or not, they want a list of their high revenue customers. Example 7 Explanation and Output Identify Most Profitable Salespeople Acme Widget wants to find out which of its salespeople are the most productive (based on profit), and give them a reward. They also want to compare productivity based on profit and based on sales. This example includes simulations. Example 8 Explanation and Output MAST Solves a Telecom Crisis Instead of using slicing-and-dicing this example describes a competitive crisis which a business might be faced with, and how MAST is used to solve the crisis in record time. MAST's simulation and low-level output abilities (customer listing and transaction details) are used. Example 9 Explanation

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