|
An example to illustrate how to find the joint probability density function of a random sample from a gamma distribution using the result that the gamma family of distributions is an exponential…
|
|
The previous lesson concluded with joint and marginal distributions for the discrete case. We now consider the same concepts, but for continuous random vectors. While discrete random vectors and the…
|
|
We discuss three techniques for constructing families of distributions. The resulting families have ready physical interpretations that make them useful for modeling as well as convenient…
|
|
The beta distribution is the last continuous distribution we will discuss. Like the gamma distribution, the beta distribution earns its name from being associated with the beta function. The beta…
|
|
In this lesson, we learn about the most important distribution in statistics - the normal distribution. We investigate the probability density function and cumulative distribution function and the…
|
|
The gamma family of distributions is a very special family that has many distributions as a specific case. In this lesson, we begin with the gamma function. We then introduce the gamma…
|
|
An overview of the uniform distribution is given, including its probability density function (PDF) and cumulative distribution function (CDF). The mean, variance, and moment generating function are…
|
|
In this lesson, a brief review of the general properties of continuous distributions is provided. The end of the lesson is a comparison of the properties for continuous and discrete distributions.
|
|
In this lesson, probability density functions are introduced, defined, and discussed. Properties of probability mass functions and probability density functions are presented. Concepts are…
|