thanks,flatmaster said:I assume the graph is suposed to match up with the numbers that the axes were labeled with, so I would go with your equations.
A probability distribution function (PDF) is a mathematical function that describes the likelihood of a random variable taking on a certain value or falling within a certain range of values. It represents the probabilities of all possible outcomes of an experiment, with the total area under the curve being equal to 1.
A discrete probability distribution function describes the probabilities for a discrete random variable, which can only take on a finite or countably infinite number of values. A continuous probability distribution function, on the other hand, describes the probabilities for a continuous random variable, which can take on an infinite number of values within a given range.
A probability distribution function and a probability density function (PDF) are two terms that are often used interchangeably, but there is a slight difference between the two. A PDF is the derivative of a probability distribution function, and it represents the relative likelihood of a continuous random variable taking on a specific value within a given range.
A probability distribution function is used to model and analyze real-world phenomena that involve randomness or uncertainty. It allows us to calculate the probabilities of various outcomes and make predictions based on the likelihood of those outcomes occurring.
Some common types of probability distribution functions include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. Each of these distributions has different characteristics and is used to model different types of data in various fields of science and statistics.