Probability distribution function referred as p(x) which is a function that satisfies below properties:
Probability that x can obtain particular value is p(x).
p[X,x]=p(x)=px
p(x) positive for all real x.
Sum of p(x) over all feasible values of x is 1, that is
`sum_(pj)pj=1`
Where, j- all possible values that x can have and pj is probability at xj.
One significance of properties 2,3 is that 0 <= p(x) <= 1.
Steps used for how to determine probability distribution:
Using following steps used to how determine probability distribution:
Step 1 for how to determine probability distribution:
Plot the data for a image illustration of the data type.
Step 2 for how to determine probability distribution:
First steps is to determining what data distribution one has - and thus the equation type to use to model the data - is to rule out what it cannot be.
The data sets cannot be a discrete uniform distribution if there are any crest in the data set.
The data is not Poisson or binomial if the data has more than one crest.
If it contains a single arc, no secondary crests, and contains a slow slope on each side, it may be Poisson or a gamma distribution. But it is not discrete uniform distribution.
If the data is regularly distributed, and it is without a slant in the direction of one side, it is secure to rule out a gamma or Weibull distribution.
If the function has an even distribution or a crest in the center of the graphed outcomes, it is not a geometric distribution or an exponential distribution.
If the incidence of a factor differ with an environmental variable, it probably is not a Poisson distribution.
Step 3 for how to determine probability distribution:
After probability distribution type has been tapering downward, do an R squared examination of each probable type of probability distribution. The one with the maximum R squared value is most possible correct.
Step 4 for how to determine probability distribution:
Remove one outlier data point. Now recalculate R squared. If the same probability distribution form comes up as the neighboring match, then there is high confidence that this is the correct probability distribution to use for group of data.
From above steps we can understand how to use probability distribution.