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Data Analysis and Probability Scenarios in Organizational Decision-Making

In this assignment, we explore a comprehensive analysis of employee data, revealing crucial insights into job levels, education, and sector distribution. This data-driven examination also delves into the intricate details of salary statistics, emphasizing the presence of non-normality. Furthermore, probability scenarios related to TV life, service time, and orange delivery are meticulously assessed. The analysis extends to process capability, ensuring it aligns with industry benchmarks, production process stability, and predictive time series forecasting. This multifaceted investigation underscores the indispensable role of data analytics in informed decision-making and process improvement, ultimately driving organizational success.

Problem Description

This data analysis assignment using probability delves into a comprehensive analysis of employee data, encompassing job level, education, and sector distribution. It provides a detailed exploration of salary statistics, highlighting non-normality and the need for further investigation. Additionally, it delves into probability scenarios for TV life, service time, and orange delivery. The analysis extends to process capability, production process stability, and a time series forecast, underscoring the significance of data-driven insights for organizational success.

Question 1: Employee Data Analysis

The objective of this analysis is to gain insights from employee data, focusing on various variables including Job Level, Sector, Education, and Salary.

1.1 Job Level Distribution

Job Level Count
Junior 83
Manager 295
Senior 304
Total 682

Job Level Distribution Table

This chart represents the distribution of employees across different job levels. It indicates that out of the 682 employees, 83 are in the junior role, 295 in managerial positions, and 304 hold senior positions.

Chart of education of employees

Fig 1: Chart of education of employees

1.2 Education Level

Education is categorized as follows:

  • Bachelor’s: 38%
  • Master’s: 37%
  • Doctorate: 25%
Graph from Pie chart

Fig 2: Pie chart showing the percentage of employees in both private and public sector

This pie chart illustrates the proportion of employees with various education levels, highlighting that 38% have a Bachelor’s degree, 37% hold a Master’s degree, and 25% have a Doctorate.

1.3 Sector Distribution

Sector Percentage
Private 74%
Public 26%

Sector Distribution Table

Histogram of salary frequency

 Fig 3: Histogram of salary frequency

The chart displays the distribution of employees in private and public sectors. It shows that 74% of employees work in the private sector, while 26% are employed in the public sector.

1.4 Salary Statistics

Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3 Maximum Skewness Kurtosis
Salary 682 0 13,310 270 7,039 4,830 8,171 10,471 17,581 30,534 1.07 -0.17

Salary Statistics Table

The table presents descriptive statistics for the 'Salary' variable. It includes key metrics such as mean, standard error of the mean, standard deviation, minimum, 1st quartile, median, 3rd quartile, and maximum. Additionally, it provides measures of skewness and kurtosis to understand the distribution.

1.5 Salary Distribution Analysis

Histogram of Salary

Probability Plot of Salaries

Fig 4: Probability Plot of Salaries

The histogram of salaries suggests non-normality. The distribution is right-skewed, indicating that salaries are concentrated on the lower end.

Normality Test

To validate the observation from the histogram, a test of normality was conducted. The result, presented in the probability plot, indicates that the variable 'Salary' is not normally distributed since the p-value (0.05) is less than the significance level.

1.6 Age Analysis

Box Plot of Age

Boxplot of age

Fig 5: Boxplot of age

The box plot of 'Age' indicates there is no evidence of outliers, suggesting that no employees fall outside the typical age range, ensuring a balanced age distribution.

1.7 Pareto Chart

A Pareto chart helps distinguish the "vital few" from the "trivial many." In this context, it demonstrates that both Engineering and IT collectively account for more than half (52.1%) of the total salary.

Pareto chart of total salary

Fig 6: Pareto chart of total salary

Question 2A: TV Life Expectancy Analysis

2.1 Probability Calculations

  • P(X < 3,350): 0.2266
  • P(X > 3,750): 0.1056
  • P(3,350 < X < 3,750): 0.6677

2.2 Finding X for P(X < x) = 0.95

If P(X < x) is 0.95, then x is calculated as 3,828.97. Therefore, the TV life expectancy is approximately 3,828.97 units.

Question 2B: Service Time Analysis

2.1 Probability Calculations

  • P(Service < 6 min): 0.8223
  • P(Service > 4 min): 0.7227
  • P(4 min < X < 6 min): 0.5450

2.2 Finding X for P(X < x) = 0.95

If P(X < x) is 0.95, then x is calculated as 6.95 minutes, which implies serving approximately 9 customers per hour.

Question 2C: Orange Delivery Analysis

To meet the contract terms of delivering at least 20 tons of oranges every week:

 P(X > 20): 0.7827

If P(X < x) is 0.95, then x is calculated as 27.8 tons per week. Therefore, there is a 95% confidence that 27.8 tons of oranges will be delivered each week.

Question 3: Process Capability Analysis

  • The data is normally distributed (p-value = 0.233).
  • The process is not centered (Ppk ≠ Pp) but is in statistical control (Ppk ≈ Cpk).
  • Process capability does not meet the benchmark (Cpk< 1.33).
  • Process improvement is needed to meet customer expectations.

Questions 4A & 4B

Box-cox plot of cycle time

Fig 7: Box-cox plot of cycle time

Johnson Transformation for Cycle Time

Fig 8: Johnson Transformation for Cycle Time

Question 5: Production Process Stability Analysis

Control Chart of Number of Cars Produced

Fig 9: Control Chart of Number of Cars Produced

The control chart demonstrates that the daily production process of cars is stable, with only common-cause variation and no out-of-control points.

Question 6: Time Series Analysis

Trend Analysis Plot for Sales

Fig 10: Trend Analysis Plot for Sales

The time series plot reveals an upward trend with no evident seasonal variation. The model accuracy, with a Mean Absolute Percentage Error (MAPE) of 8%, suggests a well-fitted model.


In conclusion, this analysis of employee data and various probability scenarios provides valuable insights. We've examined job level distribution, education, sector distribution, salary statistics, and conducted probability calculations for TV life, service time, and orange delivery. Additionally, we've assessed process capability, production process stability, and a time series forecast. These findings emphasize the importance of data-driven decision-making and process improvement in meeting and exceeding organizational objectives.