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Summarizing and Presenting Data Essay Sample

Summarizing and Presenting Data Pages
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The data collected in the BIMS case study had two major errors. The first error was when the office support staff member made the decision to use “0” if the employee did not answer the question. Two out of the ten questions received a response from each employee who completed the survey. About 17 employees provided a no response in eight of the questions and that is about 21.8% of the responses. The second error was when the office support staff member entered “6” when the response was really “5”. This data entry error had made the responses in five questions seem skewed. Even with these two major errors in the raw data, the negative responses were the highest percentage in all of the questions. The average negative responses were almost 30%. The top two highest negative responses were in questions nine (41%) and six (38%). The employees who responded felt that BIMS is not good at communicating and they also felt that they weren’t being paid fairly for the work they do at the medical center. Measures of Central Tendency and Variability

From the data in the chart we can comprise some figures that show validity towards the fact that BIMS employees did not believe in the communication of the company and that weren’t being properly paid for. The central tendency of the questions had a mean average of answers that directly fell under the majority of negative responses. The arithmetic figure towards the mean of “very negative” responses in questions 1-10 was 15.6, which does not correlate well to the “very positive” responses in questions 1-10 that were 10.3. In the pie chart it is evident that the variance between negative and positive responses are greater in favor of negative responses. Using the proper methods of collecting the data and understanding how to illustrate percentages and how to spread out data that may be clustered; we can find that solutions show the picture of the data as seen in the variability to the responses.

Conclusion
After careful analysis of the data collected BIMS has come to certain answers from the data drawn from the survey and will also draw its conclusions based on these answers in our reply to the change in management action. Firstly, the tactic used by BIMS has derived the desired information properly for taking the next step in the managerial decision. While there are some facts that re known to the team about BIMS outside of the collected data, the team is using the data collected from the survey above to draw its conclusions. Stating that perspective, there is a non-response bias that may be skewing the data.

As stated above, of all employees surveyed only 17.3% filled out the questions, which draws us to our first conclusion that our information gathered may not be meaningful enough to have the impact needed for direct change. In the graphs pulled from BIMS and our own calculated data generated from the survey, the first serious of employees showed that time worked at the company while the second set of employees represent the cumulative score that are thought as key factors. Even though a highly ranked employee filled out the survey, the scores have remained flat with a mean score of 25.576. As an indicator for positive responses and act as the heartbeat for the sample population, the methodology for being behind the key to this indicator is to use the questions one through ten, excluding question four.

References

University of Phoenix. (2015). Ballard Integrated Managed Services Inc Part 1. Retrieved from University of Phoenix, QNT351 – Quantitative Analysis for Business website. McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for Business and Economics (11th ed.). Retrieved from The University of Phoenix eBook Collection database.

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