The Excess Kurtosis of the Geometric distribution with parameter p is:
For a Geometric distribution with parameter \(p\) (counting failures before first success, support \(k=0,1,2,\ldots\)), the standard formula for excess kurtosis is:
\[\gamma_2 = \frac{p^2}{1-p} + 6\]
This is derived from the fourth central moment divided by the square of the variance, minus 3. Matching with the options, the answer is \(6 + \dfrac{p^2}{1-p}\).
Similar Questions
If the data are skewed, which option of central tendency measure is the most unreliable indicator?
For a distribution, the mean is 10, variance is 16, γ 1is + 1 and β 2is 4. The distribution is:
For the discrete distribution, the Pearson's coefficient of skewness β 2is always:
60% of the employees of a company are college graduates. Of these, 10% are in sales. Of the employees who did not graduate from college, 80% are in sales. The probability that an employee selected at random is in sales, is:
The probability that a contractor gets a plumbing contract is 2 / 3 and the probability that he will not get an electric contract is 5 / 9. If the probability of getting at least one contract is 4 / 5, then the probability that he will get both the contracts is:
For a frequency distribution of a variable x, mean = 32, median = 30. The distribution is:
The first four raw moments of distribution are 2, 136, 320, and 40,000, The coefficient of skewness is:
For a distribution, the percentile partition values are P 10 = 58.983, P 50 = 61.345 and P 90 = 63.831. Kelly's coefficient of skewness is:
A box contains four soccer balls printed with numbers 112, 121, 211. 222. A footballer chooses one ball at random. Let A 1be the event that the first digit of the printed number of the ball chosen is 1. Similarly, A 2and A 3denote that second as well as third digit of the printed number is 1. The events A 1,A 2, and A 3are:
If the Bowley’s coefficient of skewness is less than zero, then the distribution is:
Important Questions from Basics of Probability
If the data are skewed, which option of central tendency measure is the most unreliable indicator?
Events A, Band C are mutually exclusive events such that \(P(A) = \dfrac{3x + 1}{3}, P(B) = \dfrac{1-x}{4}\)and \(P(C) = \dfrac{1-2x}{4}\)The set of possible values of x are in the interval
Let A, B be two events in a discrete probability space with ℙ(A) > 0 and ℙ(B) > 0. Which of the following are necessarily true?
For a distribution, the mean is 10, variance is 16, γ 1is + 1 and β 2is 4. The distribution is:
For the discrete distribution, the Pearson's coefficient of skewness β 2is always:
