What is continuous distribution and discrete distribution?

A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite).

What is meant by discrete probability distribution?

A discrete probability distribution counts occurrences that have countable or finite outcomes. This is in contrast to a continuous distribution, where outcomes can fall anywhere on a continuum. Common examples of discrete distribution include the binomial, Poisson, and Bernoulli distributions.

What is difference between continuous and discrete?

Discrete data is information that can only take certain values. Continuous data is data that can take any value. Height, weight, temperature and length are all examples of continuous data.

What is a continuous probability distribution called?

The equation used to describe a continuous probability distribution is called a probability density function (pdf). All probability density functions satisfy the following conditions: The random variable Y is a function of X; that is, y = f(x). The value of y is greater than or equal to zero for all values of x.

What is the main difference between a discrete probability distribution and a normal probability distribution?

Explanation: The main difference between normal distribution and binomial distribution is that while binomial distribution is discrete. This means that in binomial distribution there are no data points between any two data points. This is very different from a normal distribution which has continuous data points.

How do you know if a probability distribution is discrete?

A random variable is discrete if it has a finite number of possible outcomes, or a countable number (i.e. the integers are infinite, but are able to be counted). For example, the number of heads you get when flip a coin 100 times is discrete, since it can only be a whole number between 0 and 100.

How do you find the continuous probability distribution?

For continuous probability distributions, PROBABILITY = AREA.

1. Consider the function f(x) = for 0 ≤ x ≤ 20.
2. f(x) =
3. The graph of f(x) =
4. The area between f(x) = where 0 ≤ x ≤ 20 and the x-axis is the area of a rectangle with base = 20 and height = .
5. Suppose we want to find P(x = 15).
6. Label the graph with f(x) and x.