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Question

Consider a Poisson distribution for the tossing of a biased coin. The mean for this distribution is μ. The standard deviation for this distribution is given by

The correct answer is

√μ

Poisson Distribution Standard Deviation Explained

The question asks to determine the standard deviation for a Poisson distribution, given that its mean is denoted by the symbol $\mu$. Understanding the fundamental properties of a Poisson distribution is crucial to answer this question.

Understanding the Poisson Distribution

A Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event. While the question mentions "tossing of a biased coin," this is typically modeled by a binomial distribution; however, for the purpose of this question, we are specifically asked about the properties of a Poisson distribution itself.

Key Parameters of a Poisson Distribution

For any Poisson distribution, there is a very important relationship between its mean and its variance. Let's look at these parameters:

  • Mean ($\mu$): The average number of events in the given interval. In a Poisson distribution, the mean is typically denoted by $\lambda$ or, as given in this question, $\mu$.
  • Variance ($\sigma^2$): A measure of how spread out the numbers are from the mean. For a Poisson distribution, a unique property is that its variance is exactly equal to its mean.
  • Standard Deviation ($\sigma$): The square root of the variance. It indicates the typical distance of data points from the mean.

Calculating Standard Deviation for Poisson Distribution

Given the properties mentioned above, we can derive the standard deviation.

  • Let $X$ be a random variable following a Poisson distribution.
  • The mean of a Poisson distribution is given as $\mu$. So, $E(X) = \mu$.
  • A key property of the Poisson distribution is that its variance is equal to its mean. Therefore, $Var(X) = \mu$.
  • The standard deviation ($\sigma$) is defined as the square root of the variance.
  • Thus, $\sigma = \sqrt{Var(X)}$.
  • Substituting the variance, we get $\sigma = \sqrt{\mu}$.

Therefore, if the mean of a Poisson distribution is $\mu$, its standard deviation will be $\sqrt{\mu}$.

Summary of Poisson Distribution Properties

Parameter Formula / Value for Poisson Distribution
Mean ($E(X)$) $\mu$
Variance ($Var(X)$) $\mu$
Standard Deviation ($\sigma$) $\sqrt{\mu}$

Based on this analysis, the standard deviation for a Poisson distribution with mean $\mu$ is $\sqrt{\mu}$.

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Important Questions from Statistical Variables

  1. Which of these statements on variation is INCORRECT?

  2. For a group of 5 male residents in a society, the mean and standard deviation of their ages are 63 years and 9 years, respectively. For a group of 4 female residents, these values are 54 years and 6 years, respectively. The variance of the combined group of male and female residents is:

  3. Factory A and Factory B employ 476 and 524 employees. respectively. The average weekly salary of an employee in Factory A is $34.5 whereas for an employee in Factory B it is $28.5, The standard deviation in paying the individual salary has been recorded as $5 and $4.5 for Factory A and Factory B, respectively. Which factory has greater variability in paying individual salary?

  4. Among the options for parameters, which option is correct for population?

  5. The formula to calculate the coefficient of quartile deviation is

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