Module 05 Course Project - Business Report Presentation
Sheet1
| Sales Consultant ID | Office | Region | Tax Type | Total Contracts | Total Sales | Total Cancellations | ||||||
| 10/1/14 | 35353356 | MANHATTAN | NORTH | W2 | 10 | $30,291.00 | 1 | |||||
| 10/1/14 | 66464667 | ATLANTA | SOUTH | W2 | 4 | $39,999.00 | 0 | Criteria | Weight | |||
| 10/3/14 | 57575758 | MIAMI | FL/HI | 1099 | 11 | $92,829.00 | 1 | Paper identifies and explains sales consultants that indicate progression towards noncompliance What about the specific points for outliers, not meeting max/min in sales, too many cancellations, etc? - 20 | 15/35 | |||
| 10/3/14 | 75265980 | MANHATTAN | NORTH | W2 | 4 | $44,255.00 | 2 | Classified, grouped, and filtered data in spreadsheet I don't see your actuall classification strategy - 10 | 25/35 | |||
| 10/5/14 | 83748200 | EL PASO | SOUTH | 1099 | 0 | $0.00 | 0 | Highlighted sales consultants in spreadsheet that are noncompliant | 20 | |||
| 10/10/14 | 26354410 | MIAMI | FL/HI | 1099 | 5 | $20,100.00 | 1 | Paper meets minimum page requirement | 5 | |||
| 10/11/14 | 33407197 | Arkansas | SOUTHWEST | 1099 | 0 | $7,881.00 | 0 | Paper is APA formatted with references and citations without spelling or grammatical errors | 5 | |||
| 10/12/14 | 40278386 | Arkansas | SOUTHWEST | 1099 | 1 | $10,000.00 | 0 | Total | 70/100% | |||
| 10/13/14 | 41107218 | Tri-State | SOUTHWEST | 1099 | 11 | $83,641.00 | 5 | |||||
| 10/14/14 | 26150025 | MANHATTAN | NORTH | W2 | 13 | $0.00 | 13 | |||||
| 10/20/14 | 26354410 | MIAMI | FL/HI | 1099 | 0 | $0.00 | 0 | |||||
| 10/20/14 | 26354410 | MIAMI | FL/HI | 1099 | 7 | $44,242.00 | 0 | |||||
| 10/20/14 | 26356778 | MIAMI | FL/HI | W2 | 1 | $555.00 | 0 | |||||
| 10/20/14 | 26457518 | MANHATTAN | NORTH | W2 | 9 | $80,000.00 | 0 | |||||
| 10/20/14 | 26520168 | MANHATTAN | NORTH | W2 | 10 | $45,000.00 | 0 | |||||
| 10/20/14 | 26526109 | MANHATTAN | NORTH | W2 | 25 | $54,535.00 | 0 | |||||
| 10/25/14 | 12658262 | MANHATTAN | NORTH | W2 | 9 | $24,222.00 | 0 | |||||
| 10/25/14 | 26161204 | MANHATTAN | NORTH | W2 | 4 | $14,344.00 | 0 | |||||
| 10/25/14 | 26531823 | CHICAGO | NORTH | W2 | 1 | $6,566.00 | 0 | |||||
| 10/26/14 | 33011618 | LOUISVILLE | SOUTH | 1099 | 1 | $250.00 | 0 | |||||
| 10/26/14 | 33255099 | MANHATTAN | NORTH | W2 | 16 | $50,000.00 | 1 | |||||
| 10/28/14 | 33233704 | BRONX | NORTH | 1099 | 0 | $321.00 | 0 | |||||
| 10/28/14 | 43207148 | Arkansas | SOUTHWEST | 1099 | 3 | $600.00 | 0 | |||||
| 10/30/14 | 26325752 | MANHATTAN | NORTH | W2 | 13 | $72,990.00 | 0 | |||||
| 10/30/14 | 26621106 | NEW ORLEANS | SOUTH | W2 | 7 | $35,000.00 | 0 | |||||
| 10/30/14 | 26621106 | NEW ORLEANS | SOUTH | W2 | 10 | $87,382.00 | 1 | |||||
| 10/31/14 | 33135393 | LOUISVILLE | SOUTH | 1099 | 9 | $21,222.00 | 0 | |||||
| Classify and Analyze Data Outliers are data points that significantly differ from the other points in a sample. These data set alert statisticians that there exist experimental abnormalities or massive errors in the type of measurement taken. When such abnormalities are noted, it might be relevant to omit outliers from the data set (Gupta, Gao, Aggarwal, and Han, 2014). An examination of the fence points and data shows that these points exceed the upper inner fence as well as the lower inner fence hence standing out as mild and extreme outliers in my data set. Equally, the outliers are sales consultants that indicate a trend towards non-compliance are from the following regions namely: Arkansas 7881, El Paso 0, Manhattan and Miami 0, Miami 555, Louisville250, Chicago 6566, Bronx 321, Miami $92,829, Manhattan 80,000, New Orleans 87,382, and Arkansas 600. When I expressed these data in a graph in excel outliers were far away from the other values. These values scared far away from the other data and therefore could not be used as the other data in interpretation. Even though the majority of the data set do not form a straight line, the above-identified outliers cannot in any way play a role in constructing a line (Hodge and Austin, 2004). In summary, the outliers in the data set must be investigated because they often contain valuable information about the entire research process, data, or the process under investigation. Finding out the reason why such abnormalities exist is necessary before axing out outliers. They are of course lousy data points especially in a graph but are appropriate in speaking about the process or the techniques used. References Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2014). Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering, 26(9), 2250-2267. Hodge, V., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial intelligence review, 22(2), 85-126. | ||||||||||||
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