Introduction to Data and Tabulation

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Links of powerpoint slides presented during class

Session 1-Introduction of Data

Session 2 Data analysis

Any business is exposed to data that may be relevant or irrelevant. This data can be related to business environment in which a business operates and comprises different set of conditions and circumstances that can be economic factors, social factors, political factors, legal factors, technological factors, demographic factors, natural factors, competition, market channels, etc. A business needs to select only relevant information from the above mentioned factors that could directly and indirectly affect the business in the long-run. This information can be further utilized for certain business decisions. Business decisions could be strategic decisions, control decisions and operational decisions. Strategic decisions of a business are related to the overall environment in which a business operates and control decisions are related to necessary decisions pertaining to specific managerial aspects of a business whereas operational decisions are related to planning production and sales of a business.

Data can be qualitative and quantitative. Qualitative data is typically known to be non-numerical in comparison with quantitative data, which is numerical in nature. Qualitative data mostly includes data that are descriptive in nature and can be expressed through words or text, photographs, videos, sound recordings, etc. Qualitative data is however, related to quantitative data and cannot be studied in isolation. Most quantitative data is based upon qualitative judgments, assumptions or hypotheses. Alternatively, most qualitative data can be described and/or manipulated in numerical terms. For example: Measurement of consumer satisfaction with mobile network service. Consumer satisfaction is an abstract term which can be identified from qualitative inputs such as factors that influenced consumers for considering a particular mobile service. These factors can include, proximity of the service provider, less number of drop calls, family/friends have the same service and have been satisfied with the service, good deals on the services, focus on specific applications, etc. These factors can be further coded in numerical terms and measured to identify the number of consumers who gave more importance to good deals, or applications, peer pressure, etc.

Relevant information identified from qualitative and quantitative data should be logically presented and contained that further enables measurement and analysis of data and could suggest conclusions and support decision making. Accordingly, data can be tabulated or presented as a table containing lists of information across rows and columns. This process is known as “tabulation”. Tabulation is as statistical table with logical listing of related quantitative data in vertical columns and horizontal rows of numbers with sufficient explanatory and qualifying words, phrases and statements in the form of titles, headings and footnotes to make clear the full meaning of the data and their origin.”  The main purpose of tabulation is to summarize and present data is to see whether there are patterns in them that can prompt effective decisions

There are three types of tabulation – Simple; Double and; Complex (including Cross-tabulation)

1)      Simple – Simple tabulation is when the data are tabulated to one characteristic. For example, the class survey conducted on November 11, 2011 that determined the frequency or number of students owning different brands of mobile phones like Blackberry, Nokia, Iphone, etc.

2)      Double – Double tabulation is when two characteristics of data are tabulated. For example, frequency or number of girls and boys in the class owning different brand of mobile phones like Blackberry, Nokia, Iphone, etc.

3)       Complex – Complex tabulation of data that includes more than two characteristics. For example, frequency or number of girls, boys and the total class owning different brand of mobile phones like Blackberry, Nokia, Iphone, etc. Crosstabulations, is also a sub-type of complex tabulation that includes cross-classifying factors to build a contingency table of counts or frequencies at each combination of factor levels. A contingency table is a display format used to analyse and record the possible relationship between two or more categorical variables. For example, the class survey conducted on November 11, 2011 determined frequency or number of students owning different brands of mobile phones across boys and girls of ages 17, 18 and 19 (Please Refer to slides no. 9 and10. in the session 2 presentation) . The purpose of this crosstabulation could be an assumption that boys and girls own certain mobile brands due to a particular age group they represent (Please Note: This assumption however may not hold true due to limited number of observations).

 

REFERENCES

  1. Statistics for Management by Richard I. Levin and David S. Rubin
  2. Business Statistics by R. S. Bharadwaj