Negative Binomial random variable

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SUMMARY

The discussion focuses on modeling fish catch data using a Negative Binomial random variable, denoted as X~Neg Bin(k,p). The expected value and variance are defined as E[X]=k(1-p)/p and Var(X)=k(1-p)/p^2, respectively. To conduct a hypothesis test, estimates K` and P` for parameters k and p must be derived from the sample mean x` and sample variance s^2. The relationship E[X]/Var = p and k=pE[X]/(1-p) is established as a method to calculate these estimates.

PREREQUISITES
  • Understanding of Negative Binomial distribution
  • Familiarity with statistical concepts of mean and variance
  • Basic knowledge of hypothesis testing
  • Proficiency in statistical software for data analysis
NEXT STEPS
  • Learn how to derive estimates for k and p from sample statistics
  • Explore hypothesis testing methods for Negative Binomial distributions
  • Study the application of Negative Binomial models in real-world scenarios
  • Investigate statistical software options for fitting Negative Binomial models
USEFUL FOR

Statisticians, data analysts, and researchers involved in ecological studies or any field requiring modeling of count data using Negative Binomial distributions.

sam_0017
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Data is collected on the number of fish caught per day on a month long fishing expedition. It is hypothesised that the data are consistent with a negative Binomial random variable ,X , starting at 0, so that X~Neg Bin(k,p) where E[X]=k(1-p)/p and Var =k(1-p)/p^2 . However, before a hypothesis test can be performed, estimates of k and p (denoted by K`and P`, respectively) are required. Show how you can obtain K` and P` based on the sample mean x` and sample variance s^2 of the fishing data.
 
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E[X]/Var = p and k=pE[X]/(1-p).
 

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