07 loại Distributions trong luyện thi PMI-RMP PMP

Probability distributions (Phân phối xác suất) là một chủ đề trong luyện thi PMP hay PMI-RMP. Phân phối này thể hiện một dãy số và cơ hội xảy ra. Để thi PMP hay PMI-RMP bạn cần phải nhớ tên và thuộc tính của mỗi loại Distributions. Sau đây là các loại Distribution bạn cần phải nhớ:

1. Triangular Distribution: estimate three numbers for an event: a most likely, upper and lower limit. Use this distribution if you dont have any experience data, but only opinions of experts in the field. => Triangular Distribution = (P+M+O)/3. The user defines the minimum, most likely, and maximum values.  Values around the most likely are more likely to occur.  Variables that could be described by a triangular distribution include past sales history per unit of time and inventory levels.
2. System Triangular distribution:
3. Asymmetrical Triangular Distribution: Frequency used in Simulation because it is easy to create from Expert Judgment. It also provides a better estimation than Uniform Distribution because the “most likely” data to occur have more weight (importance) than other data.
4. Uniform Distribution: You can use it when you have minimum and maximum value but no value in between has a greater change of occurring than another. All values have an equal chance of occurring, and the user simply defines the minimum and maximum.  Examples of variables that could be uniformly distributed include manufacturing costs or future sales revenues for a new product.
5. Beta Distribution (PERT Estimation): Like the Triangular distribution, beta distribution gives more weight to most likely value. => Beta Distribution = (P + O + 4M)/6
6. Normal Distribution (Bell Shaped Distribution): This distribution resembles many real life facts(like the height of people) where majority of the results are close to the averages. The user simply defines the mean or expected value and a standard deviation to describe the variation about the mean.  Values in the middle near the mean are most likely to occur.  It is symmetric and describes many natural phenomena such as people’s heights, inflation rates and energy prices.
7. Lognormal Distribution: a special form of normal distribution and frequency used for reliability applications like those on equipment failure. Values are positively skewed, not symmetric like a normal distribution.  It is used to represent values that don’t go below zero but have unlimited positive potential.  Examples of variables described by lognormal distributions include real estate property values, stock prices, and oil reserves. Lognormal and Uniform use Standard deviations and means.

Note:

• SD = (P-O)/6
• Variance = SD^2