The binomial distribution and the negative binomial distribution are both discrete probability distributions used to model the probability of success in a sequence of independent and identically distributed Bernoulli trials. Both distributions are characterized by the probability of success (p) and the number of trials (n). However, there are some key differences between the two

The negative binomial distribution is a discrete probability distribution that describes the probability of a given number of failures occurring before a given number of successes in a sequence of independent and identically distributed Bernoulli trials. It is often used to model the number of failures that occur before a certain number of successes in

The Weibull distribution is a continuous probability distribution that is commonly used in reliability engineering and statistical analysis. In addition to the two-parameter Weibull distribution, there is also a three-parameter Weibull distribution. The third parameter is the location parameter, also known as the threshold parameter, which determines the point at which the distribution begins. When the

The Bernoulli distribution is a discrete probability distribution that describes the probability of a binary outcome (such as success or failure). It is named after Jacob Bernoulli, a Swiss mathematician.Bernoulli TrialA Bernoulli trial is a statistical experiment with only two possible outcomes: success or failure. Bernoulli trials are often used to model the outcome of a

The Weibull distribution is a continuous probability distribution that is commonly used in reliability engineering and statistical analysis. It is named after Waloddi Weibull, who developed the distribution to describe the strength of materials. The Weibull distribution has two parameters: shape and scale. The shape parameter, often denoted by the symbol \(\alpha\), determines the shape of

The Hypergeometric Distribution is a probability distribution used to model the number of successes in a sample drawn from a finite population without replacement. Properties of the Hypergeometric Distribution: The Hypergeometric Distribution is defined by three parameters: the population size (N), the number of successes in the population (K), and the sample size (n). It has several

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