Nishan Poojary
Shri Madhwa Vadiraja Institute of Technology and Management (SMVITM), Udupi
Nishan Poojary has created this Calculator and 300+ more calculators!
Mona Gladys
St Joseph's College (St Joseph's College), Bengaluru
Mona Gladys has verified this Calculator and 400+ more calculators!

10 Other formulas that you can solve using the same Inputs

Standard deviation of hypergeometric distribution
Standard Deviation=sqrt((Number of items in sample*Number of success*(Number of items in population-Number of success)*(Number of items in population-Number of items in sample))/((Number of items in population^2)*(Number of items in population-1))) GO
Standard deviation of proportion
Standard deviation of proportion=sqrt((Probability of Success*(1-Probability of Success))/(Number of items in population)) GO
Standard deviation of proportion given probability of success
Standard deviation of proportion=sqrt((Probability of Success)*(Probability of Failure )/(Number of items in population)) GO
Variance of negative binomial distribution.
Variance of distribution=(Number of success*Probability of Failure )/(Probability of Success^2) GO
Standard deviation of negative binomial distribution
Standard Deviation=sqrt((Number of success*Probability of Failure )/(Probability of Success)) GO
variance of proportion
Variance=(Probability of Success*(1-Probability of Success))/(Number of items in population) GO
variance of proportion given probability of success
Variance=(Probability of Success)*(Probability of Failure )/(Number of items in population) GO
Mean of hypergeometric distribution
Mean of data=(Number of items in sample*Number of success)/(Number of items in population) GO
Mean of negative binomial distribution
Mean of distribution=(Number of success*Probability of Failure )/Probability of Success GO
Sample mean
Mean of data=Sum of observation/Number of items in sample GO

11 Other formulas that calculate the same Output

Sample variance
Variance=((sum of difference btw ith term and sample mean^2)/(Number of elements in population-1)) GO
Variance of population
Variance=(sum of difference btw ith term and sample mean^2)/Number of elements in population GO
variance of proportion
Variance=(Probability of Success*(1-Probability of Success))/(Number of items in population) GO
Variance of population proportion given probability of success
Variance=(Probability of Success*(1-Probability of Success))/Number of trials GO
variance of binomial distribution
Variance=Number of trials*Probability of Success*(1-Probability of Success) GO
Variance population proportion
Variance=(Probability of Success*Probability of Failure )/Number of trials GO
Proportion Of Variance
Variance=1-Residual sum of squares/Total sum of squares GO
Variance
Variance=((Pessimistic time-Optimistic time)/6)^2 GO
Variance Using Z-Score
Variance=((Value of A-Mean of data)/Z-score)^2 GO
Variance Of Data
Variance=(Standard Deviation)^2 GO
Variance of Poisson distribution
Variance=Mean of data GO

Variance of hypergeometric distribution Formula

Variance=((Number of items in sample*Number of success*(Number of items in population-Number of success)*(Number of items in population-Number of items in sample))/((Number of items in population^2)*(Number of items in population-1)))
σ<sup>2</sup>=((n*z*(N-z)*(N-n))/((N^2)*(N-1)))
More formulas
Common ratio in geometric progression GO
Sum of infinite geometric progression GO
mean of binomial distribution GO
variance of binomial distribution GO
Standard deviation of binomial distribution GO
Mean of negative binomial distribution GO
Variance of negative binomial distribution. GO
Standard deviation of negative binomial distribution GO
Mean of geometric distribution GO
Variance of geometric distribution. GO
Standard deviation of geometric distribution GO
Mean of hypergeometric distribution GO
Standard deviation of hypergeometric distribution GO
Mean of Poisson distribution GO
Variance of Poisson distribution GO
Standard deviation of poisson distribution GO
t-statistic GO
Standardized test statistics GO
One sample z test for proportion GO
Two sample z test for proportion GO
one sample t test for means GO
Two sample t test for means GO
One sample t-test GO
Two sample test, pooled standard deviation GO
Simple linear regression test slope GO
Chi square goodness of fit test GO
Chi square test for homogeneity GO
Chi square test for independence GO
Mean of sampling distribution of mean GO
Mean of sampling distribution of proportion GO
Standard deviation of proportion GO
Standard deviation of proportion given probability of success GO
population standard deviation GO
variance of proportion GO
variance of proportion given probability of success GO

What is statistics?

Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied.

How to Calculate Variance of hypergeometric distribution?

Variance of hypergeometric distribution calculator uses Variance=((Number of items in sample*Number of success*(Number of items in population-Number of success)*(Number of items in population-Number of items in sample))/((Number of items in population^2)*(Number of items in population-1))) to calculate the Variance, The Variance of hypergeometric distribution formula is defined by the formula v = (( n * k * (N - K)* (N - n)) / (( N^2)) * ( N -1)) where n is the number of items in the sample, N is the number of items in the population and K is the number of success in the population. Variance and is denoted by σ2 symbol.

How to calculate Variance of hypergeometric distribution using this online calculator? To use this online calculator for Variance of hypergeometric distribution, enter Number of items in sample (n), Number of success (z) and Number of items in population (N) and hit the calculate button. Here is how the Variance of hypergeometric distribution calculation can be explained with given input values -> 1.199495 = ((50*5*(100-5)*(100-50))/((100^2)*(100-1))).

FAQ

What is Variance of hypergeometric distribution?
The Variance of hypergeometric distribution formula is defined by the formula v = (( n * k * (N - K)* (N - n)) / (( N^2)) * ( N -1)) where n is the number of items in the sample, N is the number of items in the population and K is the number of success in the population and is represented as σ2=((n*z*(N-z)*(N-n))/((N^2)*(N-1))) or Variance=((Number of items in sample*Number of success*(Number of items in population-Number of success)*(Number of items in population-Number of items in sample))/((Number of items in population^2)*(Number of items in population-1))). Number of items in sample is the count of how many numbers are are there in a sample, Number of success is the number of times the desired outcome has appeared in a given number of trials and Number of items in population is the count of how many numbers are are there in a population.
How to calculate Variance of hypergeometric distribution?
The Variance of hypergeometric distribution formula is defined by the formula v = (( n * k * (N - K)* (N - n)) / (( N^2)) * ( N -1)) where n is the number of items in the sample, N is the number of items in the population and K is the number of success in the population is calculated using Variance=((Number of items in sample*Number of success*(Number of items in population-Number of success)*(Number of items in population-Number of items in sample))/((Number of items in population^2)*(Number of items in population-1))). To calculate Variance of hypergeometric distribution, you need Number of items in sample (n), Number of success (z) and Number of items in population (N). With our tool, you need to enter the respective value for Number of items in sample, Number of success and Number of items in population and hit the calculate button. You can also select the units (if any) for Input(s) and the Output as well.
How many ways are there to calculate Variance?
In this formula, Variance uses Number of items in sample, Number of success and Number of items in population. We can use 11 other way(s) to calculate the same, which is/are as follows -
  • Variance=((Pessimistic time-Optimistic time)/6)^2
  • Variance=(Standard Deviation)^2
  • Variance=1-Residual sum of squares/Total sum of squares
  • Variance=((Value of A-Mean of data)/Z-score)^2
  • Variance=Number of trials*Probability of Success*(1-Probability of Success)
  • Variance=(Probability of Success*Probability of Failure )/Number of trials
  • Variance=Mean of data
  • Variance=(Probability of Success*(1-Probability of Success))/Number of trials
  • Variance=(sum of difference btw ith term and sample mean^2)/Number of elements in population
  • Variance=((sum of difference btw ith term and sample mean^2)/(Number of elements in population-1))
  • Variance=(Probability of Success*(1-Probability of Success))/(Number of items in population)
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