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What is difference between continuous and discrete?
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 are examples of discrete data?
Examples of discrete data include the number of people in a class, test questions answered correctly, and home runs hit. Tables, or information displayed in columns and rows, and graphs, or structured diagrams that display the relationship among variables using two axes, are two ways to display discrete data.
What’s the difference between discrete and quantitative data?
On the other hand, quantitative data is one that contains numerical values and uses range. It is sub-classified as discrete and continuous data. Discrete data contains finite values that have nothing in-between. As against, continuous data contains data that can be measured, that includes fractions and decimals.
Can you have both discrete and continuous data?
Discrete data is data that can only take certain values, while data that can take any value is continuous data. 2. Can we have both discrete data and continuous data from the same experiment? If we have quantitative data, such as the correct number of questions on a test, then the data can be continuous or discrete.
Which is the best definition of continuous data?
Continuous data can take on any value as it’s measured. Your weight. Your weight can be any weight within the range of human weights. Your weight is not a specific fixed number.
What is a discrete date?
Discrete date, on the other hand, can only take on integer values, and it is typically things counted in whole numbers. Discrete data is based on counts where only a finite number of values is possible. Discrete Data can only take certain values.