Business Report Presentation with Findings and Recommendations

Course Project – Classify and Analyze Data

Course Project – Classify and Analyze Data

Course Project – Classify and Analyze Data

Francis Orig

Rasmussen College

This paper is being submitted on October 20, 2016, Janell Robinson B288/GEB2888 Section 01 Introduction to Business Analysis and Intelligence – Online Plus – 2016 Fall Quarter Term 1

Most scientific research normally aims at investigating a particular phenomenon in order to ascertain the causal and effect of how things behave the way they do. In this case, data collected must be recorded, interpreted and analyzed for the purpose of presenting the information or results to the relevant stakeholders (Peck, Olsen & Devore, 2015).. Therefore, spread sheet program is the most effective especially when computing statistical data. In this scenario, the data represents sales data for various states for a period October, November and December for the year 2014. The data as per the attached spread sheet has been sorted to aid compilation and interpretation. The data has been sorted out on the basis of sales cancellation. Thus, a stakeholder who has been presented with this information is able to view at a glance the total sales cancellation as per given state. Based on the sorted data, the trend which is evident is that the frequency of cancellation is low for high value sales while huge as compared to value sales with high level of cancellations. The reason for such identification is because they tend to stand out given the nature of data. Elsewhere, some of the sales consultants who have exhibited noncompliance include, Vega and Heston just to mention a few. Consequently, majority of them who show noncompliance are likely to have zero returns in terms of sales turnover over the stated periods. In a nutshell, the attached sales data show a great asymmetry as the whole data is positively skewed (Newcomer, Hatry & Wholey, 2015). Most of the cancellations do not relate to high value sales and this makes the entire data to become symmetrical.

References

Newcomer, K. E., Hatry, H. P., & Wholey, J. S. (2015). Handbook of practical program evaluation. John Wiley & Sons.

Peck, R., Olsen, C., & Devore, J. L. (2015). Introduction to statistics and data analysis. Cengage Learning.