Difference between revisions of "Manuals/calci/BINOMIALPROBABILITY"
Jump to navigation
Jump to search
(Created page with "<div style="font-size:25px">'''MANNWHITNEYUTEST(xRange,yRange,Confidencelevel,Logicalvalue,Testtype)'''</div><br/>") |
|||
(3 intermediate revisions by one other user not shown) | |||
Line 1: | Line 1: | ||
− | <div style="font-size:25px">''' | + | <div style="font-size:25px">'''BINOMIALPROBABILTY(NumberOftrials,NumberOfSuccess,ProbabiltyOfSuccess)'''</div><br/> |
+ | *<math>Numberoftrials</math> is the trials occured. | ||
+ | *<math>NumberofSuccess</math> is the success occured. | ||
+ | |||
+ | ==Description== | ||
+ | *This function gives the probability value of the Binomial distribution. | ||
+ | *A binomial experiment has the following characteristics: | ||
+ | *1.The experiment involves repeated trials. | ||
+ | *2.Each trial has only two possible outcomes - a success or a failure. | ||
+ | *3.The probability that a particular outcome will occur on any given trial is constant. | ||
+ | *4.All of the trials in the experiment are independent. | ||
+ | *A binomial probability refers to the probability of getting EXACTLY r successes in a specific number of trials. | ||
+ | *The number of trials refers to the number of attempts in a binomial experiment. | ||
+ | *The number of trials is equal to the number of successes plus the number of failures. | ||
+ | *When computing a binomial probability, it is necessary to calculate and multiply three separate factors: | ||
+ | *1. the number of ways to select exactly r successes, | ||
+ | *2. the probability of success (p) raised to the r power, | ||
+ | *3. the probability of failure (q) raised to the (n - r) power. | ||
+ | *The formula for Binomial probability is: | ||
+ | <math>\binom{n}{r}p^r.q^{n-r}</math> or :<math>\binom{n}{r}p^r(1-p)^{n-r}</math> | ||
+ | *where n = number of trials,r = number of specific events you wish to obtain. | ||
+ | *p = probability that the event will occur, q = probability that the event will not occur.(q = 1 - p, the complement of the event) | ||
+ | |||
+ | |||
+ | ==Examples== | ||
+ | #BINOMIALPROBABILTY(5,3,0.4)=0.23040000000000005 | ||
+ | #BINOMIALPROBABILTY(10,4,0.25)=0.1459980010986328 | ||
+ | #BINOMIALPROBABILTY(12,11,0.75)=0.12670540809631348 | ||
+ | |||
+ | ==Related Videos== | ||
+ | |||
+ | {{#ev:youtube|zJm-IXdMYOI|280|center|Binomial Distribution}} | ||
+ | |||
+ | ==See Also== | ||
+ | *[[Manuals/calci/LEVENESTEST| LEVENESTEST]] | ||
+ | *[[Manuals/calci/MOODSMEDIANTEST| MOODSMEDIANTEST]] | ||
+ | *[[Manuals/calci/RIEMANNZETA| RIEMANNZETA]] | ||
+ | |||
+ | ==References== | ||
+ | *[http://en.wikipedia.org/wiki/Binomial_distribution Binomial probability] |
Latest revision as of 14:16, 18 July 2015
BINOMIALPROBABILTY(NumberOftrials,NumberOfSuccess,ProbabiltyOfSuccess)
- is the trials occured.
- is the success occured.
Description
- This function gives the probability value of the Binomial distribution.
- A binomial experiment has the following characteristics:
- 1.The experiment involves repeated trials.
- 2.Each trial has only two possible outcomes - a success or a failure.
- 3.The probability that a particular outcome will occur on any given trial is constant.
- 4.All of the trials in the experiment are independent.
- A binomial probability refers to the probability of getting EXACTLY r successes in a specific number of trials.
- The number of trials refers to the number of attempts in a binomial experiment.
- The number of trials is equal to the number of successes plus the number of failures.
- When computing a binomial probability, it is necessary to calculate and multiply three separate factors:
- 1. the number of ways to select exactly r successes,
- 2. the probability of success (p) raised to the r power,
- 3. the probability of failure (q) raised to the (n - r) power.
- The formula for Binomial probability is:
or :
- where n = number of trials,r = number of specific events you wish to obtain.
- p = probability that the event will occur, q = probability that the event will not occur.(q = 1 - p, the complement of the event)
Examples
- BINOMIALPROBABILTY(5,3,0.4)=0.23040000000000005
- BINOMIALPROBABILTY(10,4,0.25)=0.1459980010986328
- BINOMIALPROBABILTY(12,11,0.75)=0.12670540809631348