How to calculate the Probability distiribution

Hi,

I have got this below question from one of the mock test.I am not able to get the calculation.

 

Given that completion time of a particular project has an expected value of 8 weeks and a standard deviation of two weeks ,and assuming that completion time is normally distributed,what is the probability of completing the project within 10 weeks

A.0.5
B.0.84
C.0.95
D.0.997

 

Please let me know the explanation

 

 It is good question, and I think - it is little different , than so far asked.

There are mainly four pobability distibution curve - mentioned in PMBOK also.

  1. Normal distribution curve
  2. Beta distibution 
  3. uniform distibution
  4. Tringular 

question is saying - normally distributed -- means normal distribution curve

It is same like we say for 

+/- 1 sigma = 68.26 % area means 0.6826 proability, in figure if you will see left side of the mean (middle pick) is half of this 68.26 - 34.13%, and  right side of the mean is also half of this 68.26 = 34.13%. lfet side called - (minus ) one sigma , right side called +(plus) one sigma.

 like

+/-2 sigma = 95.46 % area means 0.9546 probability

-------------------------------------------------------------------------------

now come to the question

sigma = 2 weeks 

10 weeks means 8+2 week = mean + right side one sigma 

corrosponding area 

Left side complete area of curve up to mean ,is half of full bell shape curve = 50%

Right side 1 sigma area = 34.13%

=> 50+34.13 = 84.13, hence total probability is 0.8413,  Answer is  B

refer both figures of following thread: 

http://er-sspawar.blogspot.in/2012_05_29_archive.html

Pawar,

Thanks for your reply with the explanation.Indeed i was not aware of this much details and since you come from Civil engineering experience it helped me a lot.I am continuously attending the mock test and now i am able to answer on quetions with the similar format.

I have one more question likely the same which will be posted after some time.

 

Thanks,

Mani

 

 You are right.  

Definitely it is easy to learn maths part for an Engineer.

What is your next question.

do we get such type of questions in real exam?

Do we get such type of questions in the PMP exam.Definately it will cosume time for non-maths proficients and other non subject matter experts?

 

Mani

 I believe this is not a lengthy or time taking question.

If your concepts are good then it is a half minute question

See given, SD =2, Mean = 8 , where will stay 10.

it is very easy now when you have been understood the concept.

50 (mean) +34(one sigma rt side)

 

Pawar,

Yes,understanding the question is key point.I understood after you have given the explanation for this question.

Likely if you have any such questions in other probablity distribution (ex: beta..etc.) please share in this forum.

 

Mani

 

 

Honestly am not clear about  this concept.Can you tell em where can I check this?Any link?I do not understand why this bell shpe,what is 50 %  etc...Can you pls send me soem link to get thos eidea?

I just know how to calculate SD  but i  can not map this to that bell shaped curve.

 

Tahnks in advance...

 

 

Hello Pawar ji

Honestly am not clear about  this concept.Can you tell em where can I check this?Any link?I do not understand why this bell shpe,what is 50 %  etc...Can you pls send me soem link to get thos eidea?

I just know how to calculate SD  but i  can not map this to that bell shaped curve.

 

Tahnks in advance...

 you can refer youtubes on normal distribution curve and  web sites mathisfun, wolform etc

for 50% remember NMD curve have mean value (mue) in middle , and either side of it have equal space 50-50 below curve based on x axis.

remember 

6sigma = 99.99966/2 = 49.99999% not 50%

it is infinite curve.

 Sunita ji 

you want to know how bell shape curve formed?

I try my best to explain this:

for example you are checking1000 tests given by pmp aspirants.

your standard value of profeciency  is suppose 150 out of 200

this standard value 150 will be called as MEAN value.

now you observe that --- some students are performing better than this , some are below this.

then you make a table of marks at X axis against frequency of students obtaining that marks at Y axis

 


Values = Marks at X axis

Frequency =Nos of students at Y axis

100


30

110


45

120


100

130


120

140


135

150


150

160


130

170


125

180


90

190


45

200


30

Total


1000

 

 If it comes in following shape --- it will be called as normally distributed.


 

                                                 

 

                                                   

 

                                                 

 

                                                   

 

                                                 

 

                                                   

 

                                                 

 

                                                   

 

                                                 

 

 

                                                 

 

                                               

 

 

 

 

                                               

 

                                             

 


 


 

   

 

                                             

 

                                           

 


 


 

 

     

 

                                           

 

                                           

 


 


 

 

                                                   

 

                                         

 


 


 


 

 

       

 

                                         

 

                                       

 


 


 


 


 

 

         

 

                                       

 

                                       

 


 


 


 


 

 

         

 

                                       

 

                                       

 


 


 


 


 

 

                                                   

 

                                     

 


 


 


 


 


 

 

           

 

                                     

 

                                     

 


 


 


 


 


 

 

                                                   

 

                                   

 


 


 


 


 


 


 

 

             

 

                                   

 

                                   

 


 


 


 


 


 


 

 

                                                   

 

                                 

 


 


 


 


 


 


 


 

 

               

 

                                 

 

                               

 


 


 


 


 


 


 


 


 

 

                 

 

                               

 

                               

 


 


 


 


 


 


 


 


 

 

                                                   

 

                             

 


 


 


 


 


 


 


 


 


 

 

                   

 

                             

 

                             

 


 


 


 


 


 


 


 


 


 

 

                                                   

 

                           

 


 


 


 


 


 


 


 


 


 


 

 

                     

 

                           

 

                           

 


 


 


 


 


 


 


 


 


 


 

 

                       

 

                         

 

                           

 


 


 


 


 


 


 


 


 


 


 

 

                       

 

                         

 

                         

 


 


 


 


 


 


 


 


 


 


 


 

 

                                                   

 

                       

 


 


 


 


 


 


 


 


 


 


 


 


 

 

                         

 

                       

 

                       

 


 


 


 


 


 


 


 


 


 


 


 


 

 

                         

 

                       

 

                   

 


 


 


 


 


 


 


 


 


 


 


 


 


 


 

 

                             

 

                   

 

 

 

   

 

     

 

 

 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 

                                                   

 

 

 

 

 

 

 

 

 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 

                               

 

 

               

 

 

 

 

 

 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 

                                     

 

           

 

 

 

 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 


 

                                         

 

 

     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                                               

µ

MEAN