This is what makes the data discrete – there are a finite number of values that can be assigned.ĭiscrete data is most often plotted with the typical types of graphs you see – bar graphs, scatter plots, and pie charts. While this data can be averaged, which is where the whole idea of households having 2.3 kids comes from, it isn’t actually possible for a household to have a partial child. And this makes discrete data inherently less precise than continuous data. It can be 10.5, for instance, but as shoe sizes are only in wholes and halves, there’s a limit to what it can be. A measurement such as someone’s shoe size is a discrete variable, as there are only so many values that it can hold. This is not to say that a discrete variable can’t have a decimal point in it. It isn’t possible to have 2.4 cars, nor is it possible to drill down to be more precise than the number of cars they have. This type of data is typically used in situations such as counting how many of something there are, such as a census, or how many cars a family owns. Some common examples of discrete data are the number of children in a household, how many games a sports team won in a season, or the number of contestants in a race.Ĭommon examples of continuous data are a person’s age, a baby’s birth weight, and the amount of time it takes a sprinter to run a quarter mile.ĭiscrete data will remain constant over an interval of time.Ĭontinuous data will vary over time and have different values at different times.ĭiscrete data variables are always fixed. All of these show individual values.Ĭontinuous data is usually represented visually by histograms or line charts, which are better for showing how the data fits together in a range. Example showing the definition of RthJC in JESD51 Test method based on MIL-STD-883E METHOD 1012. High performance heat sink Lead frame Thermal interface material (TIM) Chip Mold Junction temperature: T J Case temperature: T C RthJC Fin Figure 1. It also doesn’t tend to vary over time – and won’t, over a set interval.ĭiscrete data is visually represented by charts such as bar graphs, pie charts, and scatter plots. ROHM’s discrete products mainly use RthJC and RJC. This means that it’s used for values that can be counted, such as how many cars a household owns. This is because it includes a range – someone’s height can be measured more and more precisely and additionally will change over time.įor discrete data, it’s individual numbers. Ordinal variable: a variable used to store discrete measurements that can be ordered from. Statistics and data processing have a tendency to take on an air of mystique, but once someone explains the basic concept of what makes a data set discrete or continuous to you, it’s not a difficult concept to grasp.Ĭontinuous data variables are typically used for measurements, such as someone’s height or weight. Observation: data from an individual study subject or sampled unit. Both are quantitative or numerical data, meaning that they are represented with numbers and can be processed mathematically. Discrete and continuous are both terms that are used in data processing in order to describe different sets of data and variables.
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