Dinah YoungProject Manager / Software Asset Manager| Prince William CountySpringfield, Va, United States
I am usually really good with the calculations and such from the PMI tests. But one thing I have never quite understood is standard deviation. What is it, what is it supposed to represent, what is it used for. I see a bell curve and 1-3 Sigmas. But I am just not understanding. Can someone explain it to me in simple terms? Saving Changes...
Normally you won't need it for the PMP, but from memory you are studying for the RMP so you will need to know it. Saving Changes...
Nehal ManekDirector Program Planning| Barnes and Noble Digital EducationEast Meadow, Ny, United States
Dec 28, 2017 2:29 PM
Replying to Dinah Young
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OK. Now can you help me apply this to Risk. I am reading that probability distribution estimates the potential of a risk event to occur over a pre-described range. What would we be mapping in regards to risk?
I am struggling to get my head around this topic.
I'll try explaining this with a very simple example :
Key activity when performing quantitative risk assessment, is to quantify uncertainty.
If we were describing the uncertainty present in the number of rainy days in a year, this probability distribution function would map each integer between 0 and 365 to a probability.
Example : You are the PM of a bridge construction project and work cannot be done on rainy days. As a PM you would like to quantify this uncertainty.
Data set collected in the past indicating # of rainy days in a year is following a normal distribution. So, higher the standard deviation the greater the probability of risk-event to occur. Since sigma refers to the +/- value from mean, the greater the range the greater the risk event to occur.
Now, the table mentioned below should make sense : Risk increases as SD increases. Mean = 20 days SD = 2 1 Sigma = 18 to 22 days 2 Sigma = 16 to 24 days 3 Sigma = 14 to 26 days
SD = 3 1 Sigma = 17 to 23 days 2 Sigma = 14 to 26 days 3 Sigma = 11 to 29 days
SD is a measure of variation. Smaller SD means that data points are tightly clustered around their mean (average) whereas a large SD means that data points are widely distributed around the mean.
The (Worst Case - Best Case)/6 formula you learned for the PMP exam is a rough approximation of SD which assumes you are dealing with a normally distributed population.
Kiron Saving Changes...
Dinah YoungProject Manager / Software Asset Manager| Prince William CountySpringfield, Va, United States
Thank all so much. I think I am finally starting to understand. I need to focus on the mean and the standard deviation. Then I can figure out the sigma's.
It is starting to make sense to me.
Hi Dinah, Standard deviation is a quantity that expresses how far are the members of a group from the mean. To understand standard deviation we need to understand variance. Variance is the average of the squared differences from the mean. Standard deviation is just the square root of variance. For normal distribution on the bell curve the mean represents the center and the height and the width of the curve depend on standard deviation. If you have a bigger value of standard deviation your curve will be wider because the members are further away from the mean. Hope this helps. Saving Changes...
George LewisProgram/Project Manager| DXC Technology CompanyHeredia, Costa Rica
Dec 28, 2017 1:26 PM
Replying to Bruce Wentworth
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A standard deviation (calculated from the sample) estimates sigma (the usually unknown attribute of the population from which the sample was drawn). For the bell shaped curve (normal distribution), plus or minus 1 sigma should cover 68%, plus or minus 2 sigmas should cover 95%, and plus or minus 3 sigmas should cover 99.7%.
Does that help/make sense?
yes, indeed... Saving Changes...
George LewisProgram/Project Manager| DXC Technology CompanyHeredia, Costa Rica
Dec 28, 2017 2:32 PM
Replying to mohan muniswamaiah
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Before we understand standard deviation, we have to understand why standard deviation was introduced.
Let's hear a story, 4 piece of chocolate is available to eat, we have 4 people to eat the same.
One person does not eat the chocolate due some reason and other three will eat 4 pieces of chocolate equally..
Now if we take average, then one person should have eaten one chocolate each. But in reality only 3 persons have eaten the chocolate.
Average = total number people / no of chocolates =4/4=1.. will not give a correct solution.
Now to avoid such confusion, Standard Deviation came in to existence.
Now how to find how many people had to chocolate... using standard deviation
A define procedure is available on how to calculate standard deviation, when you calculate using that method, you arrive a number for example 2.98
A graph called bell curve is drawn by use of numbers generated from standard deviation calculation. The graph looks like a bell curve. now the central portion of bell curve is the average ( in crude terms or )standard deviation . the distance which amounts to 2.98 ( example number) with in the center of the graph or bell curve is called standard deviation.
Standard deviation is a number which covers 68% area of bell curve.
In above example, in vague terms we can tell that, 68% of " 4 people group " would have eaten at least one piece of chocolate
Well explained... Saving Changes...
Anonymous
Hi Dinah
I will give you a simple example
If you record the time it takes you to commute to work every day and continue recording for 100 days - you get 100 data point for commuting in one direction and 100 points for the other direction.
Assuming there are no major out of the norm issue on a given commute your time will vary maybe between 30 and 40 min. sometime it will be 31, or 39 or 35 or 36. This data can be represented via a normal distribution curve and the mean will be the middle point.
A mean - means that 50% of your data will be on the left and 50% on the right of the mean. Say the mean is 35 min then 50% will be less than 35 and 50% more than 35.
I keep forgetting how to calculate the Standard Deviation but say it is 1 min.
Now a 1 Sigma Process, means that 68% of your commute trips will be between 34 and 36 min (+/- 1 Sigma). 2 Sigma means 95% of your trip will be between 33 and 37 min and so on.
You can translate the above to $ or duration or hours, etc.
Does this help?
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1 reply by Zeeshan Amjad
Jan 23, 2018 2:53 PM
Zeeshan Amjad
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Good real life example Mounir, but with one little problem. The example is actually for median not for mean. If you need 50% on the left and 50% data on the right then it is actually median.
Let me give try to explain. Supposed you commute 10 days with the following commute time
30, 38, 35, 35, 37, 96, 35, 33, 34, 38
As you can see there is an out liar on day 6, may be an accident on that day and it took 96 minutes to reach the office on that day.
Now the mean of this is 41 and median of this is 35. If you take a look at the mean which is 41, then there is only one value greater than 41 and rest of the values are less than that.
However in case of median, 50% values are smaller than 35 as well as 50% values are greater than this.
Median is a great tool to remove the out liar in the data. For this reason usually median salary and median property value is reported to avoid any skewness because of one or more out lairs.
You can always calculate the higher moment, such as skewness (3rd moment) or kurtosis (4th moment) to get more insight about the data, but it is more calculation and well beyond the PMP outline.
Regards
Zeeshan Amjad
Saving Changes...
Nehal ManekDirector Program Planning| Barnes and Noble Digital EducationEast Meadow, Ny, United States
Dec 28, 2017 7:59 PM
Replying to Dinah Young
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Thank all so much. I think I am finally starting to understand. I need to focus on the mean and the standard deviation. Then I can figure out the sigma's.
It is starting to make sense to me.
Great .. Saving Changes...
Dinah YoungProject Manager / Software Asset Manager| Prince William CountySpringfield, Va, United States
You are all so great. The way I tend to learn new concepts is to start with a the core and then my brain can build off of it.
I have re-read all of the responses and the concept is becoming clearer and clearer. For the purpose of the RMP test I now understand that the greater the standard deviation, the greater the risk. The Standard deviation is based from the mean and is derived from the worst case and optimistic estimates as is the variance. The percentages that are the only thing I remember about SD from my PMP training is just stating the normal distribution in the sigmas. I will go back and review the actual formulas. I have not re-memorized them all yet. Concepts first, memorization later is my study plan.
I am on my own studying for this test, so it is great that I can come here and ask questions when I feel stumped.
Thanks again. Saving Changes...
