Why use a discrete representation for continuous quantities?

1. Why do we use discrete data?
2. What is difference between discrete and continuous data?
3. What is a discrete representation of data?
4. How should continuous data be presented?

Why do we use discrete data?

Discrete data is often used in simple statistical analysis because it's easy to summarize and compute. Let's look at some of the other key characteristics of discrete data. Discrete data includes discrete variables that are finite, numeric, countable, and non-negative integers (5, 10, 15, and so on).

What is difference between discrete and continuous data?

The primary difference, though, between discrete and continuous data is that discrete data is a finite value that can be counted whereas continuous data has an infinite number of possible values that can be measured.

What is a discrete representation of data?

Discrete data is a type of quantitative data that includes nondivisible figures and statistics you can count. You typically write discrete data points as numbers that represent exact values. You can often describe discrete data by using the phrase "the number of," such as the number of customers in a store.

How should continuous data be presented?

Continuous data are measured on a scale or continuum (such as weight or test scores). Histograms are useful for displaying continuous data. Bar graphs, line graphs, and histograms have an x- and y-axis. The x-axis is the horizontal part of the graph and the y-axis is the vertical part.