What is the difference between categorical and continuous variable

To focus on individual months, treat time as discrete and use bars. To look at trends and the rate of change and thus, the space in between the data points , use continuous time. Line and bar charts can appear to be interchangeable, but they are usually not. The encoding is subtly different length for the bars, position for the line , and there is a clear implication in the line that there is a continuum between the points. Using a line chart for the product type chart above would not make sense, since there is nothing in between Espresso and Herbal Tea.

Even if we only have one data point for each month, though, time is still continuous, so we can treat it as such if we want. We often want to see more than two data attributes at the same time. Categorical axes can be used to break data down further. Each category is subdivided by the categories of the additional dimensions.

Adding two categorical dimensions, Market and Year to the initial chart gives us a lot more bars. Here, time is now categorical, which means we get separate bars for each year. There are other ways to show the same data: we could stack the bars for the different product groups, for example. Which dimensions are nested, and in what order, is also important. We could decide that we want to see each product type broken down by market instead, rather than the other way around, or maybe break each year down into markets, and look at the products across those combinations.

Which is the right configuration depends on the question you want to ask. But the type of visualization has not changed, we are still looking at bars. Adding categorical dimensions to a visualization usually divides the visualization up rather than changing the type.

The axis mappings have not changed, they are still continuous time and profit. But adding the product type subdivides the total into four separate lines. We can now see how each of them have done over time, which ones are flat, which increasing, etc.

Adding color is not strictly necessary here, but it makes following the lines and identifying them much easier.

Color works great for categories, at least as long as the number is reasonably small. These examples are very straight-forward. Simple charts tend to work well for a small number of data dimensions. More unusual encodings should only be used when more variables are needed. The scatterplot shows two numerical values using position along each axis.

This shows me that the West market had the highest sales in all but the Coffee category look at the locations of the X marks compared to the other shapes of the same color , though not always the highest profits. Like color, shape works well for a small number of categories, because we can really only tell a very limited number of them apart 10 is roughly the maximum for both.

If we wanted to add another quantitative dimension, we might use size, though that would start to overload the chart. It is usually a better idea to keep the number of visual variables like color, shape, size, orientation, etc. It is often more effective to create several different charts or rethink the question to make sure all these dimensions are really needed at the same time.

This is not to say that it is impossible to do so, but it will either be impractical or unethical to do so.

For example, a researcher may be interested in the effect of illegal, recreational drug use the independent variable s on certain types of behaviour the dependent variable s. However, whilst possible, it would be unethical to ask individuals to take illegal drugs in order to study what effect this had on certain behaviours. As such, a researcher could ask both drug and non-drug users to complete a questionnaire that had been constructed to indicate the extent to which they exhibited certain behaviours.

Whilst it is not possible to identify the cause and effect between the variables, we can still examine the association or relationship between them. In addition to understanding the difference between dependent and independent variables, and experimental and non-experimental research, it is also important to understand the different characteristics amongst variables.

This is discussed next. Categorical and Continuous Variables Categorical variables are also known as discrete or qualitative variables. Nominal variables are variables that have two or more categories, but which do not have an intrinsic order. For example, a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows.

So "type of property" is a nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note, the different categories of a nominal variable can also be referred to as groups or levels of the nominal variable. Another example of a nominal variable would be classifying where people live in the USA by state. In this case there will be many more levels of the nominal variable 50 in fact.

Dichotomous variables are nominal variables which have only two categories or levels. For example, if we were looking at gender, we would most probably categorize somebody as either "male" or "female". This is an example of a dichotomous variable and also a nominal variable. Another example might be if we asked a person if they owned a mobile phone.

Here, we may categorise mobile phone ownership as either "Yes" or "No". In the real estate agent example, if type of property had been classified as either residential or commercial then "type of property" would be a dichotomous variable.

Ordinal variables are variables that have two or more categories just like nominal variables only the categories can also be ordered or ranked. So if you asked someone if they liked the policies of the Democratic Party and they could answer either "Not very much", "They are OK" or "Yes, a lot" then you have an ordinal variable. Because you have 3 categories, namely "Not very much", "They are OK" and "Yes, a lot" and you can rank them from the most positive Yes, a lot , to the middle response They are OK , to the least positive Not very much.

However, whilst we can rank the levels, we cannot place a "value" to them; we cannot say that "They are OK" is twice as positive as "Not very much" for example. Quantitative variables can be classified as discrete or continuous. Categorical variable Categorical variables contain a finite number of categories or distinct groups. Categorical data might not have a logical order. For example, categorical predictors include gender, material type, and payment method.

Discrete variable Discrete variables are numeric variables that have a countable number of values between any two values. A discrete variable is always numeric.