How are continuous and discrete treated differently?

Question

Audrey Allen · Accepted Answer

Yest. The continuous and discrete random variables are treated differently.

A random variable is said to be discrete if the set of values assigned to the random variable is countable. Further, for every single value of the random variable, there is an associated probability. Hence to derive the constants of the discrete random variable, we use #sum#. The set of values assigned to a continuous random variable is uncountable. The probability is obtained for a range (interval) of values and not for individual values. Hence, to find the constants, we use #int#.

Liam Burke · Answer

undefined Continuous and discrete data are treated differently in various contexts, particularly in mathematics, statistics, and computer science.

Continuous data refers to data that can take on any value within a certain range, often represented by real numbers. Continuous data is measured and can have an infinite number of possible values between any two points. Discrete data, on the other hand, refers to data that can only take on specific, distinct values and cannot be broken down into smaller parts.

In mathematical analysis, continuous functions are those that are defined for all real numbers within a certain interval, while discrete functions are defined only for specific points or values.

In statistics, continuous data is often analyzed using techniques such as calculus-based methods for finding integrals and derivatives, while discrete data is analyzed using techniques such as probability distributions and combinatorics.

In computer science, continuous data may be represented using floating-point numbers, while discrete data may be represented using integers or other data types that can only take on specific values.

Overall, the treatment of continuous and discrete data differs based on their unique characteristics and the specific context in which they are being analyzed or utilized.