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Apply Quantitative Reasoning

  1. From the created histogram, it appears that a large share of employees have a salary between $61,000–$140,000 or $141,000–$190,000. This may indicate a reasonable promotion rate for new employees. Is this distribution unimodal or bimodal? Please explain.

- The histogram created from the given data represents the unimodal distribution. It is because there is only one real peak point distinguished from the employees' salary distribution at $81,000 to $90,000. Some may define this distribution as bimodal or even multimodal because there are slight increases shown at $141,000 to $150,000 mark, and $161,000 to $170,000 mark. However, when the trends line, as shown below, is added to the histogram to represent the moving average, the trends line displays right-skewed unimodal distribution.

Apply Quantitative Reasoning Image 1
  1. The line chart, as detailed in your "Graph Charts" Excel spreadsheet, shows sales generally increasing over the years, although sales in the first two years were notably lower. Assuming that the sales are linear, please use the Forecast tool to find projected sales for 2019 and 2020. Hint: An easy way to do this is to highlight the sales from the data page and apply the Forecast tool to this data. Your output will be a chart and a new sheet with projected sales for average, upper, and lower range growth.

- Based on the assumption that the sales are linear, the forecasting tool from Excel calculated that the expected sales in 2019 are around $60,000,000, and the expected sales in 2020 are approximately 62,000,000.

Apply Quantitative Reasoning Image 2
  1. The standard deviation provides insight into the distribution of values around the mean. If the standard deviation is small, the more narrow the range between the lowest and highest value. That is, values will cluster close to the mean. From your descriptive statistics, describe your standard deviations. What does this tell you about the variables?

- The standard deviation calculated from the employees' salary data is 32,350.20. The salary graph marks every $10,000 and the standard deviation represents the majority of salaries are within the three marks upper and lower from the average salary; that is between $70,000 to 130,000. This range shows that the majority of salaries are distributed within 35% of the total range. Thus, as the salary distribution does not spread out over 50% of the total range, the salaries are cluster close to the mean.

- The summary statistic calculated for the hourly rate represents the same distribution as the employees' salary data; salary converted into the hourly rate, so it shows the same result as the employees' salary data.

- The summary statistic calculated for the employees' years of service represents a standard deviation value of 4.08. This value displays the majority of variables are distributed in the range between from the employees work at the company for 3 years to 11 years of service. When the oldest employee has been serving the company for more than 15 years, the graph's range is set from 0 to 16 years of service. As a result, the majority of variables are spread out over 50% of the total range, resulting that the variables of employees' years of service does not cluster close to the mean.

- The summary statistic calculated for the employees' educational level has a standard deviation value of 1.72. When over 80% of employees with an Associate or Bachelor degree, and when the mean is calculated to be in between Associate and Bachelor degree. The variables are highly clustered to the mean.

- The summary statistic calculated for the employees' age represents a standard deviation value of 9.7, and the calculated mean as 35.83. While the youngest employee is 18, and the oldest is 62 years old, the actual range of this distribution is 44 years. The majority of employees are between 26 to 46 years olds, and they are spread out within 45% of the total range. As the variables do not spread over in excess of 50% of the range, the variables are cluster close to the mean.

  1. The company has a keen interest in the educational, race, and gender makeup of its workforce. Its emphasis is on a diverse, dynamic workforce. From your "Graph Charts" spreadsheet, describe your pie chart findings for these characteristics of the workforce. Describe how you would determine if the company was meeting expectations on these characteristics.

- For educational diversity and the dynamic workforce, the company is meeting the expectations. As there are a limited number of managers and senior-level jobs available, likely, these positions are assigned to those who achieved higher levels of education. A small number of employees have masters, or higher education level is excusable. For the junior to mid-level of workforces are gathered with some Highschool Diploma, Associate's Degree, and Bachelor's Degree. As those employees with the Highschool Diploma are maybe the interns, about an equal amount of positions are distributed to both Associates and Bachelors.

- For racial diversity and the dynamic workforce, the company is not meeting expectations as the majority of workforces are Caucasian, which is 61% of total employees. 26% of total employees are African-Americans, and the rests with Asian and Hispanic, only filling the 6% and 7% gap, respectively. Only a small portion of employees are non-Caucasian races.

- For gender diversity and the dynamic workforce, the company is not meeting the expectations, either. As 81% of workforces are male, the percentage gap between gender distribution is too big.

- For gender diversity and the dynamic workforce, the company is meeting the expectations. When only 19% of the workforce is female, and it does not look like the company is representing the gender diversity but when the gender imbalance in the cybersecurity industry is enormous; that is only 14 percent of the U.S. workforce is women (Mcclatchy, 2018). The company looked tried hard to break the gender imbalance.

Mcclatchy, T. J. (2018, January 25). Why are so few women in cybersecurity? Retrieved from https://www.govtech.com/workforce/Why-Are-So-Few-Women-in-Cybersecurity.html

  1. The company is conducting an analysis on how many positions to create to keep up with demand. Specifically, it wants to know an estimate of the number of positions per job title. From your Excel chart, identify the mode of the job title distribution. Describe your findings.

- From the excel chart, Cyber Analyst is displayed as the most occupied job title in all three locations. 

- Although the question does not explicitly describe the market demand, this analysis presumes that there are continuous growing demands from the market, and all three regions obtaining the same amount of the market demands. First of all, the company seems to keep many of the managers in the NorthEast head-quarter. To keep up with demands in three regions, the company should look out for the additional resources to allocate the same number of managerial positions in each region. These managerial positions are Engineer Manager, IT Manager, Logistics Manager, Public and Business Office Team Manager, and Senior Cybersecurity Analyst. Also, some of the junior to mid-level positions are unevenly distributed throughout the three regions, especially, the Cyber Analysts, Forensic Analysts, and Malware Reverse Engineers. Those workforces are also centrally located in the NorthEast head-quarter. To fill the gap, the company should hire additional resources in these positions. Further, as the company grows and serves more clients, five Quality Assurance specialists seem not enough to keep up with the demands. So, the company would also want to hire additional QA resources. Lastly, the Logistics team should also be reinforced with additional hiring to help the company to manage the internal and external resources properly.

Job Title

Total # of Position

C-Plain

MidWest

NorthEast

Acctg/Fin

10

3

3

4

Admin

4

1

1

2

Advertising

5

1

2

2

CEO

1

0

0

1

CFO

1

0

0

1

CIO

1

0

0

1

Controller

1

0

0

1

COO

1

0

0

1

Cyber Analyst

159

49

57

53

Cyber Mgr

7

2

2

3

Cyber Software Engineer

23

8

8

7

Eng Mgr

1

0

0

1

Forensics Analyst

40

10

11

19

Investigator

8

2

3

3

IT Mgr

1

0

0

1

IT Staff

8

2

3

3

Logistics

5

1

2

2

Logistics Mgr

1

0

0

1

Malware Reverse Engineer

18

3

5

10

Marketing

5

1

2

2

Physical Security

18

5

6

7

Public and Business Office Team

14

3

5

6

Public and Business Office Team Mgr

1

0

0

1

Quality Assurance

5

1

2

2

Sr Cyber Analyst

1

0

1

0

Sr Cyber Investigator

27

7

8

12

Sr Forensics Mgr

3

1

1

1

Sr Public and Business Office Team Mgr

3

1

1

1

TOTAL

372

101

123

148

FINAL ESSAY:

Now that you have done all the work with data, you will write a short three- to four-paragraph summary of your analysis. This is important. While you have done a wonderful job with your analysis, you can never assume that the end user will be able to interpret the data the way it should be understood. Supporting narrative is helpful. Never simply provide a "raw data" dump. Instead, seek to provide information!

The conducted quantitative analysis of the given data portrayed many interesting aspects of this company. The employees' salary data and the company's sales data reveal that the company is spending a vast amount of their revenue on keeping their employees and outsourcing talented ones. Reviewing the salary data confirmed that those highly skilled employees are receiving as much as double the amount of other colleagues receive. Additionally, one key factor that has a relevant link to their salary is the educational level. Other traits, such as age, gender, marital status, and race, did not cause a significant difference. What can be found from the data is that the company is willing to evaluate a higher salary to those skilled and talented workers, and one measurement to distinguish them is their educational level.

The company is spending about 80% of its total sales on the payroll. The company made total sales of $51,702,000 in 2015, and the total payroll calculated for 372 employees is $41,433,167. Not many companies spend such a high portion of the income on the payroll. Based on this number, it is relevant to say that the company is investing in its employees. Further, the salary gaps between employees' educational level are significant. On average, the employees with a master's or higher degree earned about $50,000 more than those who completed a bachelor's degree. The employees with a bachelor's degree received about $25,000 more than those holding an associate's degree. Lastly, employees with associate's degrees made about $17,000 more than employees with the High school Diploma.

One additional interesting fact found in this analysis is that the employee's years of service do not seem to be reflected in their salary level. Few employees worked in the same years in this company but receiving notably different salaries. Further, some employees recently joined the company and getting a higher salary than those who worked longer in the company. For example, two Senior Cyber Investigators have the same educational level, and both spent seven years in this company; one is earning about $170,000, while the other is making $140,000. Another example is that the Cyber Analyst who worked over fifteen years is receiving about $60,000, whereas another Cyber Analyst who joined the company two years ago is already making over $80,000. Both of them have the same educational level. What can be concluded from these findings is that the employee's performance and their skills play a greater role in determining their salary, and one other measurement that the company uses to determine the skilled workforces is looking at their educational level.

The above-stated analysis is limited to provide fundamental and one-dimensional analysis. Reviewing the company's ledger is required to achieve a better result from the conductive analysis. Although the given data included a good amount of information, few important statistics are missing to obtain the firm analysis. First, previous years of employees' salary data is needed to review, to compare, and find the employees' salary trends. The company's sales are showing a stagnation at around $50 million for the past several years. When 80% of the company's sales go to the payroll, it is questionable if the payroll ratio remained similar in the past years. Also, it is uncertain how the company is managing all other expenses. Eventually, this information will demonstrate that if the salary increases in the employees exceeded the company's sales growth over the past years. Another helpful statistic is the detailed expenses list. The company has only 20% of sales money available for keeping the business up and running. After the taxes are taken off, it is unsure if the company has any leftover to cover all other administrative expenses such as real estate, marketing, and insurances. In summary, to provide in-depth analysis, further research is needed on the company's ledger. Especially as the company is planning to hire more employees as the company grows, these numbers must be thoroughly reviewed, and appropriate actions must be taken before stepping forward.

Structure your essay like this:

Write a one-paragraph narrative summary of your findings, describing patterns of interest.

Provide an explanation of the potential relevance of such patterns.

Provide a description of how you would investigate further to determine if your results are "good or bad" for the company

Prepare your response in this workbook. (Simply expand this text box to accommodate your essay and other answers.)

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