Discrete and continuous characteristics both represent types of variables used in data analysis and statistics. They are similar in that they both can be used to describe and categorize data points, and they can impact the outcomes of statistical analyses. Additionally, both types can be used in various fields, such as Social Sciences, natural sciences, and engineering, to study phenomena and draw conclusions based on collected data. Ultimately, they serve the purpose of providing meaningful insights about different traits or measurements.
Continuous. Discrete variables are only expressed as integer values, whereas continuous is, as its name suggests, continuous.
In an experiment, water can be considered a continuous variable. This is because water can take on an infinite number of values within a given range, such as volume or temperature, and can be measured with varying degrees of precision. Discrete variables, on the other hand, consist of distinct, separate values, which does not apply to water in most experimental contexts.
A discontinuous variable is a variable that has distinct categories. Blood type is a good example. You could be A, B, AB or O. This contrasts with a continuous variable such as height or weight, where there are an almost infinite number of possible values. Data for discontinuous variables is usually represented using a bar graph or pie chart, but never a scatter graph.
In science, "discrete" refers to distinct, separate, and individual units or values that can be counted or categorized, rather than measured on a continuous scale. For example, discrete data might include whole numbers like the number of organisms in a population or the different species in a study, while continuous data would involve measurements like height or temperature that can take any value within a range. The concept of discreteness is important in various fields, including statistics, mathematics, and computer science, as it influences how data is analyzed and interpreted.
A discrete variable is a type of quantitative variable that can take on a finite or countable number of distinct values. Examples include the number of students in a classroom, the result of a dice roll, or the number of cars in a parking lot. Discrete variables contrast with continuous variables, which can take on any value within a given range. In statistical analysis, discrete variables are often represented by whole numbers.
They are probability distributions!
Some manufacturing is discrete, some continuous.
ocean depth is a continuous or discrete variable?
continuous discrete
Continuous.
Continuous
Continuous
discrete
discrete
discrete
Discrete.
continuous because discrete data involve a count of items