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Continuous Distribution or Continuous probability distribution is actually probability distribution in which X, the random variable can take on any value. This means X is continuous here. As there are unlimited values that X could take, the probability of any specific value of X is “zero“. So, in statistics we talk about ranges of values (p(X>0) = .50). The normal distribution is one example of a continuous distribution. The probability that X falls between the two values (a and b) is equal to the integral from a to b. This is the area under the curve that will be formed by F(X). Here, F(X) = P (a ≤ x ≤ b) = integration of F(X) within the limit (a, b).

Comprehensive Skill Essential for Solving Continuous Distributions Problems

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Important Parts of Continuous Distributions

If you have already covered some parts of the course, you have come across the most important parts of Continuous Distributions:

Exploration of continuous data: There are two types of data known in this discipline: Discrete data and Continuous data. Data with a finite or countable number of possible outcomes are known as discrete data, on the other hand, continuous data are the data that come from the interval of a possible outcome. Some examples of continuous data are the amount of rain that falls in a randomly selected day in the monsoon season or the weight of a randomly selected person on board of a flight, etc. Here, as a student, you will learn Histograms, Order statistics, Sample Percentile, Box plots, Stem and Leaf plots, Shapes of distribution, etc.
Continuous Random variables: A Continuous Random variable contains an uncountable possible outcome of infinite character. For example, if X denotes the height (in meters) of a randomly chosen alpine tree, then X can be said as a continuous random variable. Some important parts of Continuous Random Variables are Probability Density Functions, Percentile, Special Expectations, Uniform Distributions, Uniform properties, Uniform applications, etc. You will learn how to introduce probability density function as a Continuous Random Variable and many other important aspects of the topic.
Normal Probability Distributions: The normal probability distribution is one of the essential continuous distributions of statistics. It is actually a group of distributions. That means it possesses an unlimited number of distributions with different standard deviations (σ) and means (μ). As the normal distribution is a continuous distribution, it is not possible to calculate the precise probability for an outcome but instead, we can calculate a probability for a range of outcomes, e.g. the probability that a random variable X is greater than 10.

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